Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.
You will learn to:
djmodel
Input
layer and its parameter shape
.Lambda
layer and replaces the given solution with hints and sample code (to improve the learning experience).Model
.music_inference_model
one_hot
function.one_hot
with a Lambda layer instead of giving the code solution (to improve the learning experience).Model
.predict_and_sample
Please run the following cell to load all the packages required in this assignment. This may take a few minutes.
from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import *
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K
You would like to create a jazz music piece specially for a friend's birthday. However, you don't know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM network.
You will train a network to generate novel jazz solos in a style representative of a body of performed work.
You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:
IPython.display.Audio('./data/30s_seq.mp3')
We have taken care of the preprocessing of the musical data to render it in terms of musical "values."
You can informally think of each "value" as a note, which comprises a pitch and duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a "value" is actually more complicated than this--specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playing multiple notes at the same time generates what's called a "chord"). But we don't need to worry about the details of music theory for this assignment.
Run the following code to load the raw music data and preprocess it into values. This might take a few minutes.
X, Y, n_values, indices_values = load_music_utils()
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('shape of X:', X.shape)
print('Shape of Y:', Y.shape)
number of training examples: 60 Tx (length of sequence): 30 total # of unique values: 78 shape of X: (60, 30, 78) Shape of Y: (30, 60, 78)
You have just loaded the following:
X
: This is an (m, $T_x$, 78) dimensional array.
Y
: a $(T_y, m, 78)$ dimensional array
X
, but shifted one step to the left (to the past). Y
is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed into the LSTM later.n_values
: The number of unique values in this dataset. This should be 78.
indices_values
: python dictionary mapping integers 0 through 77 to musical values.
Here is the architecture of the model we will use. This is similar to the Dinosaurus model, except that you will implement it in Keras.
# number of dimensions for the hidden state of each LSTM cell.
n_a = 64
djmodel()
will call the LSTM layer $T_x$ times using a for-loop.n_values = 78 # number of music values
reshapor = Reshape((1, n_values)) # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True) # Used in Step 2.C
densor = Dense(n_values, activation='softmax') # Used in Step 2.D
reshapor
, LSTM_cell
and densor
are globally defined layer objects, that you'll use to implement djmodel()
. layer_object()
.layer_object(X)
layer_object([X1,X2])
Exercise: Implement djmodel()
.
Input()
layer is used for defining the input X
as well as the initial hidden state 'a0' and cell state c0
.shape
parameter takes a tuple that does not include the batch dimension (m
).X = Input(shape=(Tx, n_values)) # X has 3 dimensions and not 2: (m, Tx, n_values)
var1 = array1[:,1,:]
lambda_layer1 = Lambda(lambda z: z + 1)(previous_layer)
X
.z
is a local variable of the lambda function. previous_layer
gets passed into the parameter z
in the lowercase lambda
function.t
within the definition of the lambda layer even though it isn't passed in as an argument to Lambda.reshapor()
layer. It is a function that takes the previous layer as its input argument.LSTM_cell
with the previous step's hidden state $a$ and cell state $c$. next_hidden_state, _, next_cell_state = LSTM_cell(inputs=input_x, initial_state=[previous_hidden_state, previous_cell_state])
densor
. Model
object to create a model.model = Model(inputs=[input_x, initial_hidden_state, initial_cell_state], outputs=the_outputs)
# GRADED FUNCTION: djmodel
def djmodel(Tx, n_a, n_values):
"""
Implement the model
Arguments:
Tx -- length of the sequence in a corpus
n_a -- the number of activations used in our model
n_values -- number of unique values in the music data
Returns:
model -- a keras instance model with n_a activations
"""
# Define the input layer and specify the shape
X = Input(shape=(Tx, n_values))
# Define the initial hidden state a0 and initial cell state c0
# using `Input`
a0 = Input(shape=(n_a,), name='a0')
c0 = Input(shape=(n_a,), name='c0')
a = a0
c = c0
### START CODE HERE ###
# Step 1: Create empty list to append the outputs while you iterate (≈1 line)
outputs = []
# Step 2: Loop
for t in range(Tx):
# Step 2.A: select the "t"th time step vector from X.
x = Lambda(lambda x: x[:, t, :])(X) # m dewa nai, tao shape (m, Tx, n_values) akare nite hobe bola ache description e
# Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)
x = reshapor(x)
# Step 2.C: Perform one step of the LSTM_cell
a, _, c = LSTM_cell(inputs=x, initial_state=[a, c])
# Step 2.D: Apply densor to the hidden state output of LSTM_Cell
out = densor(a)
# Step 2.E: add the output to "outputs"
outputs.append(out)
# Step 3: Create model instance
model = Model(inputs=[X, a0, c0], outputs=outputs)
### END CODE HERE ###
return model
Tx=30
, n_a=64
(the dimension of the LSTM activations), and n_values=78
. model = djmodel(Tx = 30 , n_a = 64, n_values = 78)
# Check your model
model.summary()
____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_1 (InputLayer) (None, 30, 78) 0 ____________________________________________________________________________________________________ lambda_1 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ reshape_1 (Reshape) (None, 1, 78) 0 lambda_1[0][0] lambda_2[0][0] lambda_3[0][0] lambda_4[0][0] lambda_5[0][0] lambda_6[0][0] lambda_7[0][0] lambda_8[0][0] lambda_9[0][0] lambda_10[0][0] lambda_11[0][0] lambda_12[0][0] lambda_13[0][0] lambda_14[0][0] lambda_15[0][0] lambda_16[0][0] lambda_17[0][0] lambda_18[0][0] lambda_19[0][0] lambda_20[0][0] lambda_21[0][0] lambda_22[0][0] lambda_23[0][0] lambda_24[0][0] lambda_25[0][0] lambda_26[0][0] lambda_27[0][0] lambda_28[0][0] lambda_29[0][0] lambda_30[0][0] ____________________________________________________________________________________________________ a0 (InputLayer) (None, 64) 0 ____________________________________________________________________________________________________ c0 (InputLayer) (None, 64) 0 ____________________________________________________________________________________________________ lambda_2 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lstm_1 (LSTM) [(None, 64), (None, 6 36608 reshape_1[0][0] a0[0][0] c0[0][0] reshape_1[1][0] lstm_1[0][0] lstm_1[0][2] reshape_1[2][0] lstm_1[1][0] lstm_1[1][2] reshape_1[3][0] lstm_1[2][0] lstm_1[2][2] reshape_1[4][0] lstm_1[3][0] lstm_1[3][2] reshape_1[5][0] lstm_1[4][0] lstm_1[4][2] reshape_1[6][0] lstm_1[5][0] lstm_1[5][2] reshape_1[7][0] lstm_1[6][0] lstm_1[6][2] reshape_1[8][0] lstm_1[7][0] lstm_1[7][2] reshape_1[9][0] lstm_1[8][0] lstm_1[8][2] reshape_1[10][0] lstm_1[9][0] lstm_1[9][2] reshape_1[11][0] lstm_1[10][0] lstm_1[10][2] reshape_1[12][0] lstm_1[11][0] lstm_1[11][2] reshape_1[13][0] lstm_1[12][0] lstm_1[12][2] reshape_1[14][0] lstm_1[13][0] lstm_1[13][2] reshape_1[15][0] lstm_1[14][0] lstm_1[14][2] reshape_1[16][0] lstm_1[15][0] lstm_1[15][2] reshape_1[17][0] lstm_1[16][0] lstm_1[16][2] reshape_1[18][0] lstm_1[17][0] lstm_1[17][2] reshape_1[19][0] lstm_1[18][0] lstm_1[18][2] reshape_1[20][0] lstm_1[19][0] lstm_1[19][2] reshape_1[21][0] lstm_1[20][0] lstm_1[20][2] reshape_1[22][0] lstm_1[21][0] lstm_1[21][2] reshape_1[23][0] lstm_1[22][0] lstm_1[22][2] reshape_1[24][0] lstm_1[23][0] lstm_1[23][2] reshape_1[25][0] lstm_1[24][0] lstm_1[24][2] reshape_1[26][0] lstm_1[25][0] lstm_1[25][2] reshape_1[27][0] lstm_1[26][0] lstm_1[26][2] reshape_1[28][0] lstm_1[27][0] lstm_1[27][2] reshape_1[29][0] lstm_1[28][0] lstm_1[28][2] ____________________________________________________________________________________________________ lambda_3 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_4 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_5 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_6 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_7 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_8 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_9 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_10 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_11 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_12 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_13 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_14 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_15 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_16 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_17 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_18 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_19 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_20 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_21 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_22 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_23 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_24 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_25 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_26 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_27 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_28 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_29 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ lambda_30 (Lambda) (None, 78) 0 input_1[0][0] ____________________________________________________________________________________________________ dense_1 (Dense) (None, 78) 5070 lstm_1[0][0] lstm_1[1][0] lstm_1[2][0] lstm_1[3][0] lstm_1[4][0] lstm_1[5][0] lstm_1[6][0] lstm_1[7][0] lstm_1[8][0] lstm_1[9][0] lstm_1[10][0] lstm_1[11][0] lstm_1[12][0] lstm_1[13][0] lstm_1[14][0] lstm_1[15][0] lstm_1[16][0] lstm_1[17][0] lstm_1[18][0] lstm_1[19][0] lstm_1[20][0] lstm_1[21][0] lstm_1[22][0] lstm_1[23][0] lstm_1[24][0] lstm_1[25][0] lstm_1[26][0] lstm_1[27][0] lstm_1[28][0] lstm_1[29][0] ==================================================================================================== Total params: 41,678 Trainable params: 41,678 Non-trainable params: 0 ____________________________________________________________________________________________________
Expected Output
Scroll to the bottom of the output, and you'll see the following:
Total params: 41,678
Trainable params: 41,678
Non-trainable params: 0
opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
Finally, let's initialize a0
and c0
for the LSTM's initial state to be zero.
m = 60
a0 = np.zeros((m, n_a))
c0 = np.zeros((m, n_a))
Y
into a list, since the cost function expects Y
to be provided in this format list(Y)
is a list with 30 items, where each of the list items is of shape (60,78). model.fit([X, a0, c0], list(Y), epochs=100)
Epoch 1/100 60/60 [==============================] - 3s - loss: 126.0544 - dense_1_loss_1: 4.3556 - dense_1_loss_2: 4.3489 - dense_1_loss_3: 4.3525 - dense_1_loss_4: 4.3486 - dense_1_loss_5: 4.3478 - dense_1_loss_6: 4.3500 - dense_1_loss_7: 4.3546 - dense_1_loss_8: 4.3447 - dense_1_loss_9: 4.3446 - dense_1_loss_10: 4.3492 - dense_1_loss_11: 4.3459 - dense_1_loss_12: 4.3520 - dense_1_loss_13: 4.3440 - dense_1_loss_14: 4.3452 - dense_1_loss_15: 4.3511 - dense_1_loss_16: 4.3436 - dense_1_loss_17: 4.3375 - dense_1_loss_18: 4.3483 - dense_1_loss_19: 4.3377 - dense_1_loss_20: 4.3462 - dense_1_loss_21: 4.3520 - dense_1_loss_22: 4.3502 - dense_1_loss_23: 4.3409 - dense_1_loss_24: 4.3429 - dense_1_loss_25: 4.3450 - dense_1_loss_26: 4.3482 - dense_1_loss_27: 4.3415 - dense_1_loss_28: 4.3448 - dense_1_loss_29: 4.3411 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0167 - dense_1_acc_2: 0.0167 - dense_1_acc_3: 0.0667 - dense_1_acc_4: 0.0667 - dense_1_acc_5: 0.0333 - dense_1_acc_6: 0.0500 - dense_1_acc_7: 0.0167 - dense_1_acc_8: 0.0667 - dense_1_acc_9: 0.0500 - dense_1_acc_10: 0.0167 - dense_1_acc_11: 0.0667 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.0500 - dense_1_acc_14: 0.0500 - dense_1_acc_15: 0.0167 - dense_1_acc_16: 0.0833 - dense_1_acc_17: 0.0333 - dense_1_acc_18: 0.0167 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.0500 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.0500 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.1000 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.0333 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00 Epoch 2/100 60/60 [==============================] - 0s - loss: 123.5769 - dense_1_loss_1: 4.3386 - dense_1_loss_2: 4.3129 - dense_1_loss_3: 4.3015 - dense_1_loss_4: 4.2959 - dense_1_loss_5: 4.2775 - dense_1_loss_6: 4.2870 - dense_1_loss_7: 4.2842 - dense_1_loss_8: 4.2604 - dense_1_loss_9: 4.2713 - dense_1_loss_10: 4.2624 - dense_1_loss_11: 4.2565 - dense_1_loss_12: 4.2766 - dense_1_loss_13: 4.2476 - dense_1_loss_14: 4.2393 - dense_1_loss_15: 4.2542 - dense_1_loss_16: 4.2449 - dense_1_loss_17: 4.2295 - dense_1_loss_18: 4.2616 - dense_1_loss_19: 4.2361 - dense_1_loss_20: 4.2473 - dense_1_loss_21: 4.2601 - dense_1_loss_22: 4.2447 - dense_1_loss_23: 4.2530 - dense_1_loss_24: 4.2528 - dense_1_loss_25: 4.2477 - dense_1_loss_26: 4.2260 - dense_1_loss_27: 4.2330 - dense_1_loss_28: 4.2378 - dense_1_loss_29: 4.2365 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1667 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1667 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.1833 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.1833 - dense_1_acc_15: 0.1167 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1667 - dense_1_acc_20: 0.1333 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.1833 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.0667 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00 Epoch 3/100 60/60 [==============================] - 0s - loss: 117.9765 - dense_1_loss_1: 4.3195 - dense_1_loss_2: 4.2668 - dense_1_loss_3: 4.2304 - dense_1_loss_4: 4.2135 - dense_1_loss_5: 4.1721 - dense_1_loss_6: 4.1870 - dense_1_loss_7: 4.1568 - dense_1_loss_8: 4.0950 - dense_1_loss_9: 4.0911 - dense_1_loss_10: 4.0343 - dense_1_loss_11: 4.0066 - dense_1_loss_12: 4.1417 - dense_1_loss_13: 4.0105 - dense_1_loss_14: 3.9703 - dense_1_loss_15: 4.0479 - dense_1_loss_16: 4.0170 - dense_1_loss_17: 3.9486 - dense_1_loss_18: 4.1282 - dense_1_loss_19: 3.9490 - dense_1_loss_20: 4.0210 - dense_1_loss_21: 4.0495 - dense_1_loss_22: 3.9898 - dense_1_loss_23: 4.0723 - dense_1_loss_24: 4.0672 - dense_1_loss_25: 4.1160 - dense_1_loss_26: 3.7667 - dense_1_loss_27: 3.9344 - dense_1_loss_28: 3.9344 - dense_1_loss_29: 4.0387 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.2667 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1667 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.0667 - dense_1_acc_8: 0.2000 - dense_1_acc_9: 0.0500 - dense_1_acc_10: 0.1000 - dense_1_acc_11: 0.0667 - dense_1_acc_12: 0.0500 - dense_1_acc_13: 0.0333 - dense_1_acc_14: 0.0667 - dense_1_acc_15: 0.0500 - dense_1_acc_16: 0.0667 - dense_1_acc_17: 0.1167 - dense_1_acc_18: 0.0333 - dense_1_acc_19: 0.0500 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.0333 - dense_1_acc_23: 0.0167 - dense_1_acc_24: 0.0167 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.0667 - dense_1_acc_28: 0.0667 - dense_1_acc_29: 0.1167 - dense_1_acc_30: 0.0000e+00 Epoch 4/100 60/60 [==============================] - 0s - loss: 113.9281 - dense_1_loss_1: 4.2975 - dense_1_loss_2: 4.2178 - dense_1_loss_3: 4.1429 - dense_1_loss_4: 4.1190 - dense_1_loss_5: 4.0320 - dense_1_loss_6: 4.0484 - dense_1_loss_7: 3.9752 - dense_1_loss_8: 3.8184 - dense_1_loss_9: 3.8878 - dense_1_loss_10: 3.7444 - dense_1_loss_11: 3.7817 - dense_1_loss_12: 4.0666 - dense_1_loss_13: 3.8521 - dense_1_loss_14: 3.7931 - dense_1_loss_15: 3.8164 - dense_1_loss_16: 3.8421 - dense_1_loss_17: 3.9308 - dense_1_loss_18: 4.0009 - dense_1_loss_19: 3.7791 - dense_1_loss_20: 4.0035 - dense_1_loss_21: 4.0125 - dense_1_loss_22: 3.8388 - dense_1_loss_23: 3.8792 - dense_1_loss_24: 3.8098 - dense_1_loss_25: 4.0024 - dense_1_loss_26: 3.6075 - dense_1_loss_27: 3.7321 - dense_1_loss_28: 3.8430 - dense_1_loss_29: 4.0533 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1000 - dense_1_acc_9: 0.1000 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.0333 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.0833 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1000 - dense_1_acc_18: 0.0333 - dense_1_acc_19: 0.1167 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.1000 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0833 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00 Epoch 5/100 60/60 [==============================] - 0s - loss: 110.9971 - dense_1_loss_1: 4.2786 - dense_1_loss_2: 4.1756 - dense_1_loss_3: 4.0664 - dense_1_loss_4: 4.0408 - dense_1_loss_5: 3.9277 - dense_1_loss_6: 3.9440 - dense_1_loss_7: 3.8732 - dense_1_loss_8: 3.6969 - dense_1_loss_9: 3.7935 - dense_1_loss_10: 3.6383 - dense_1_loss_11: 3.6689 - dense_1_loss_12: 4.0051 - dense_1_loss_13: 3.7906 - dense_1_loss_14: 3.6758 - dense_1_loss_15: 3.7354 - dense_1_loss_16: 3.6865 - dense_1_loss_17: 3.8381 - dense_1_loss_18: 3.8710 - dense_1_loss_19: 3.6325 - dense_1_loss_20: 3.8643 - dense_1_loss_21: 3.9412 - dense_1_loss_22: 3.7208 - dense_1_loss_23: 3.6814 - dense_1_loss_24: 3.6680 - dense_1_loss_25: 3.8778 - dense_1_loss_26: 3.5557 - dense_1_loss_27: 3.6296 - dense_1_loss_28: 3.7775 - dense_1_loss_29: 3.9418 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.2667 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.0833 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1333 - dense_1_acc_15: 0.1167 - dense_1_acc_16: 0.1000 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1667 - dense_1_acc_20: 0.0833 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00 Epoch 6/100 60/60 [==============================] - 0s - loss: 108.7173 - dense_1_loss_1: 4.2603 - dense_1_loss_2: 4.1377 - dense_1_loss_3: 3.9837 - dense_1_loss_4: 3.9556 - dense_1_loss_5: 3.8518 - dense_1_loss_6: 3.8622 - dense_1_loss_7: 3.7990 - dense_1_loss_8: 3.5897 - dense_1_loss_9: 3.6675 - dense_1_loss_10: 3.5088 - dense_1_loss_11: 3.5847 - dense_1_loss_12: 3.9215 - dense_1_loss_13: 3.6381 - dense_1_loss_14: 3.5436 - dense_1_loss_15: 3.6684 - dense_1_loss_16: 3.6150 - dense_1_loss_17: 3.7010 - dense_1_loss_18: 3.7111 - dense_1_loss_19: 3.5435 - dense_1_loss_20: 3.8127 - dense_1_loss_21: 3.8574 - dense_1_loss_22: 3.6827 - dense_1_loss_23: 3.6150 - dense_1_loss_24: 3.6122 - dense_1_loss_25: 3.7893 - dense_1_loss_26: 3.5620 - dense_1_loss_27: 3.6809 - dense_1_loss_28: 3.7127 - dense_1_loss_29: 3.8492 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.1000 - dense_1_acc_11: 0.1000 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.1833 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.0667 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.1833 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.1667 - dense_1_acc_29: 0.1167 - dense_1_acc_30: 0.0000e+00 Epoch 7/100 60/60 [==============================] - 0s - loss: 105.4507 - dense_1_loss_1: 4.2455 - dense_1_loss_2: 4.1050 - dense_1_loss_3: 3.9123 - dense_1_loss_4: 3.8798 - dense_1_loss_5: 3.7527 - dense_1_loss_6: 3.7935 - dense_1_loss_7: 3.7292 - dense_1_loss_8: 3.4455 - dense_1_loss_9: 3.5460 - dense_1_loss_10: 3.3946 - dense_1_loss_11: 3.4668 - dense_1_loss_12: 3.7477 - dense_1_loss_13: 3.4155 - dense_1_loss_14: 3.3408 - dense_1_loss_15: 3.5034 - dense_1_loss_16: 3.5269 - dense_1_loss_17: 3.5532 - dense_1_loss_18: 3.5637 - dense_1_loss_19: 3.4141 - dense_1_loss_20: 3.7231 - dense_1_loss_21: 3.7686 - dense_1_loss_22: 3.5360 - dense_1_loss_23: 3.5765 - dense_1_loss_24: 3.5573 - dense_1_loss_25: 3.7345 - dense_1_loss_26: 3.4181 - dense_1_loss_27: 3.4471 - dense_1_loss_28: 3.6416 - dense_1_loss_29: 3.7115 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.2500 - dense_1_acc_14: 0.2500 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.1333 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.0833 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.1000 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.1333 - dense_1_acc_30: 0.0000e+00 Epoch 8/100 60/60 [==============================] - 0s - loss: 102.3254 - dense_1_loss_1: 4.2336 - dense_1_loss_2: 4.0710 - dense_1_loss_3: 3.8519 - dense_1_loss_4: 3.8116 - dense_1_loss_5: 3.6770 - dense_1_loss_6: 3.7078 - dense_1_loss_7: 3.6167 - dense_1_loss_8: 3.3558 - dense_1_loss_9: 3.4170 - dense_1_loss_10: 3.2720 - dense_1_loss_11: 3.3430 - dense_1_loss_12: 3.5874 - dense_1_loss_13: 3.2984 - dense_1_loss_14: 3.2741 - dense_1_loss_15: 3.4230 - dense_1_loss_16: 3.3672 - dense_1_loss_17: 3.3226 - dense_1_loss_18: 3.4792 - dense_1_loss_19: 3.2932 - dense_1_loss_20: 3.5170 - dense_1_loss_21: 3.6175 - dense_1_loss_22: 3.4155 - dense_1_loss_23: 3.4213 - dense_1_loss_24: 3.4260 - dense_1_loss_25: 3.6374 - dense_1_loss_26: 3.3527 - dense_1_loss_27: 3.4728 - dense_1_loss_28: 3.5001 - dense_1_loss_29: 3.5624 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.0833 - dense_1_acc_10: 0.1500 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.0500 - dense_1_acc_13: 0.1500 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1000 - dense_1_acc_17: 0.1833 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1167 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.1333 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.0833 - dense_1_acc_27: 0.1333 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.1000 - dense_1_acc_30: 0.0000e+00 Epoch 9/100 60/60 [==============================] - 0s - loss: 98.5030 - dense_1_loss_1: 4.2242 - dense_1_loss_2: 4.0365 - dense_1_loss_3: 3.7885 - dense_1_loss_4: 3.7399 - dense_1_loss_5: 3.5884 - dense_1_loss_6: 3.6136 - dense_1_loss_7: 3.5153 - dense_1_loss_8: 3.2418 - dense_1_loss_9: 3.3234 - dense_1_loss_10: 3.2006 - dense_1_loss_11: 3.2298 - dense_1_loss_12: 3.4298 - dense_1_loss_13: 3.1853 - dense_1_loss_14: 3.1158 - dense_1_loss_15: 3.3227 - dense_1_loss_16: 3.2866 - dense_1_loss_17: 3.2219 - dense_1_loss_18: 3.2997 - dense_1_loss_19: 3.1644 - dense_1_loss_20: 3.4281 - dense_1_loss_21: 3.4684 - dense_1_loss_22: 3.2124 - dense_1_loss_23: 3.3182 - dense_1_loss_24: 3.3030 - dense_1_loss_25: 3.4251 - dense_1_loss_26: 3.0496 - dense_1_loss_27: 3.2379 - dense_1_loss_28: 3.2084 - dense_1_loss_29: 3.3237 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.1500 - dense_1_acc_10: 0.1500 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1833 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1333 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.1500 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.1500 - dense_1_acc_21: 0.1333 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.0833 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.2500 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.1167 - dense_1_acc_30: 0.0000e+00 Epoch 10/100 60/60 [==============================] - 0s - loss: 96.0373 - dense_1_loss_1: 4.2146 - dense_1_loss_2: 3.9980 - dense_1_loss_3: 3.7261 - dense_1_loss_4: 3.6620 - dense_1_loss_5: 3.4939 - dense_1_loss_6: 3.5205 - dense_1_loss_7: 3.4199 - dense_1_loss_8: 3.1522 - dense_1_loss_9: 3.1964 - dense_1_loss_10: 3.1393 - dense_1_loss_11: 3.1154 - dense_1_loss_12: 3.3118 - dense_1_loss_13: 3.0865 - dense_1_loss_14: 2.9935 - dense_1_loss_15: 3.2095 - dense_1_loss_16: 3.2104 - dense_1_loss_17: 3.1193 - dense_1_loss_18: 3.1803 - dense_1_loss_19: 3.1356 - dense_1_loss_20: 3.3582 - dense_1_loss_21: 3.4116 - dense_1_loss_22: 3.1813 - dense_1_loss_23: 3.3171 - dense_1_loss_24: 3.2093 - dense_1_loss_25: 3.3605 - dense_1_loss_26: 2.9902 - dense_1_loss_27: 3.1135 - dense_1_loss_28: 3.0794 - dense_1_loss_29: 3.1308 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.2667 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.2500 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.2000 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.2167 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.2333 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.1667 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1500 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2500 - dense_1_acc_27: 0.0667 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00 Epoch 11/100 60/60 [==============================] - 0s - loss: 92.1433 - dense_1_loss_1: 4.2038 - dense_1_loss_2: 3.9572 - dense_1_loss_3: 3.6634 - dense_1_loss_4: 3.5814 - dense_1_loss_5: 3.4092 - dense_1_loss_6: 3.4225 - dense_1_loss_7: 3.3022 - dense_1_loss_8: 3.0266 - dense_1_loss_9: 3.0668 - dense_1_loss_10: 2.9919 - dense_1_loss_11: 2.9665 - dense_1_loss_12: 3.1949 - dense_1_loss_13: 2.9803 - dense_1_loss_14: 2.9011 - dense_1_loss_15: 3.0719 - dense_1_loss_16: 3.0124 - dense_1_loss_17: 2.9222 - dense_1_loss_18: 3.0250 - dense_1_loss_19: 2.9312 - dense_1_loss_20: 3.1185 - dense_1_loss_21: 3.2033 - dense_1_loss_22: 3.0269 - dense_1_loss_23: 3.0767 - dense_1_loss_24: 2.9867 - dense_1_loss_25: 3.2109 - dense_1_loss_26: 2.8206 - dense_1_loss_27: 3.0316 - dense_1_loss_28: 2.9254 - dense_1_loss_29: 3.1122 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.3167 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.2000 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.2333 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.2333 - dense_1_acc_21: 0.1500 - dense_1_acc_22: 0.1667 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.3333 - dense_1_acc_27: 0.2167 - dense_1_acc_28: 0.3000 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00 Epoch 12/100 60/60 [==============================] - 0s - loss: 88.0183 - dense_1_loss_1: 4.1928 - dense_1_loss_2: 3.9139 - dense_1_loss_3: 3.5961 - dense_1_loss_4: 3.4904 - dense_1_loss_5: 3.3007 - dense_1_loss_6: 3.2937 - dense_1_loss_7: 3.1823 - dense_1_loss_8: 2.8999 - dense_1_loss_9: 2.8973 - dense_1_loss_10: 2.8624 - dense_1_loss_11: 2.8600 - dense_1_loss_12: 3.0576 - dense_1_loss_13: 2.7797 - dense_1_loss_14: 2.7829 - dense_1_loss_15: 2.8661 - dense_1_loss_16: 2.9529 - dense_1_loss_17: 2.7956 - dense_1_loss_18: 2.8303 - dense_1_loss_19: 2.7707 - dense_1_loss_20: 3.0097 - dense_1_loss_21: 3.0211 - dense_1_loss_22: 2.8283 - dense_1_loss_23: 2.8286 - dense_1_loss_24: 2.7957 - dense_1_loss_25: 3.1039 - dense_1_loss_26: 2.6196 - dense_1_loss_27: 2.8191 - dense_1_loss_28: 2.7955 - dense_1_loss_29: 2.8712 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1833 - dense_1_acc_8: 0.3000 - dense_1_acc_9: 0.2833 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.1500 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.3000 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.2333 - dense_1_acc_19: 0.2833 - dense_1_acc_20: 0.2500 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.2000 - dense_1_acc_23: 0.2333 - dense_1_acc_24: 0.2167 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.3333 - dense_1_acc_27: 0.2333 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00 Epoch 13/100 60/60 [==============================] - 0s - loss: 83.6134 - dense_1_loss_1: 4.1822 - dense_1_loss_2: 3.8718 - dense_1_loss_3: 3.5250 - dense_1_loss_4: 3.3980 - dense_1_loss_5: 3.1828 - dense_1_loss_6: 3.1514 - dense_1_loss_7: 3.0542 - dense_1_loss_8: 2.7274 - dense_1_loss_9: 2.7381 - dense_1_loss_10: 2.7102 - dense_1_loss_11: 2.6682 - dense_1_loss_12: 2.8450 - dense_1_loss_13: 2.5869 - dense_1_loss_14: 2.5034 - dense_1_loss_15: 2.6988 - dense_1_loss_16: 2.7752 - dense_1_loss_17: 2.6546 - dense_1_loss_18: 2.6899 - dense_1_loss_19: 2.5724 - dense_1_loss_20: 2.7893 - dense_1_loss_21: 2.8483 - dense_1_loss_22: 2.6670 - dense_1_loss_23: 2.6699 - dense_1_loss_24: 2.6114 - dense_1_loss_25: 2.9321 - dense_1_loss_26: 2.5480 - dense_1_loss_27: 2.6844 - dense_1_loss_28: 2.5696 - dense_1_loss_29: 2.7579 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.3167 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.2667 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.2167 - dense_1_acc_13: 0.3000 - dense_1_acc_14: 0.3333 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.1833 - dense_1_acc_18: 0.2167 - dense_1_acc_19: 0.2833 - dense_1_acc_20: 0.2500 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.2000 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.2333 - dense_1_acc_25: 0.1833 - dense_1_acc_26: 0.3000 - dense_1_acc_27: 0.2167 - dense_1_acc_28: 0.2500 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00 Epoch 14/100 60/60 [==============================] - 0s - loss: 79.2699 - dense_1_loss_1: 4.1738 - dense_1_loss_2: 3.8294 - dense_1_loss_3: 3.4484 - dense_1_loss_4: 3.3002 - dense_1_loss_5: 3.0673 - dense_1_loss_6: 2.9964 - dense_1_loss_7: 2.9219 - dense_1_loss_8: 2.5981 - dense_1_loss_9: 2.5999 - dense_1_loss_10: 2.5815 - dense_1_loss_11: 2.5280 - dense_1_loss_12: 2.6735 - dense_1_loss_13: 2.4350 - dense_1_loss_14: 2.3402 - dense_1_loss_15: 2.5862 - dense_1_loss_16: 2.5726 - dense_1_loss_17: 2.4968 - dense_1_loss_18: 2.5003 - dense_1_loss_19: 2.4635 - dense_1_loss_20: 2.5314 - dense_1_loss_21: 2.6325 - dense_1_loss_22: 2.5017 - dense_1_loss_23: 2.5023 - dense_1_loss_24: 2.4465 - dense_1_loss_25: 2.7345 - dense_1_loss_26: 2.3567 - dense_1_loss_27: 2.5096 - dense_1_loss_28: 2.4000 - dense_1_loss_29: 2.5414 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.2000 - dense_1_acc_8: 0.3333 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.3000 - dense_1_acc_11: 0.3167 - dense_1_acc_12: 0.2333 - dense_1_acc_13: 0.3500 - dense_1_acc_14: 0.3667 - dense_1_acc_15: 0.2167 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.2167 - dense_1_acc_19: 0.3000 - dense_1_acc_20: 0.3000 - dense_1_acc_21: 0.2500 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.2500 - dense_1_acc_25: 0.1833 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.3000 - dense_1_acc_28: 0.3667 - dense_1_acc_29: 0.2000 - dense_1_acc_30: 0.0167 Epoch 15/100 60/60 [==============================] - 0s - loss: 75.3995 - dense_1_loss_1: 4.1642 - dense_1_loss_2: 3.7848 - dense_1_loss_3: 3.3641 - dense_1_loss_4: 3.2003 - dense_1_loss_5: 2.9435 - dense_1_loss_6: 2.8273 - dense_1_loss_7: 2.7857 - dense_1_loss_8: 2.4611 - dense_1_loss_9: 2.4537 - dense_1_loss_10: 2.4463 - dense_1_loss_11: 2.3915 - dense_1_loss_12: 2.5072 - dense_1_loss_13: 2.2814 - dense_1_loss_14: 2.2026 - dense_1_loss_15: 2.4318 - dense_1_loss_16: 2.4456 - dense_1_loss_17: 2.2869 - dense_1_loss_18: 2.3337 - dense_1_loss_19: 2.3606 - dense_1_loss_20: 2.3756 - dense_1_loss_21: 2.4747 - dense_1_loss_22: 2.3775 - dense_1_loss_23: 2.3454 - dense_1_loss_24: 2.3327 - dense_1_loss_25: 2.5396 - dense_1_loss_26: 2.2364 - dense_1_loss_27: 2.4086 - dense_1_loss_28: 2.2467 - dense_1_loss_29: 2.3900 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2500 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.2167 - dense_1_acc_8: 0.3000 - dense_1_acc_9: 0.4000 - dense_1_acc_10: 0.3167 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.3333 - dense_1_acc_13: 0.4167 - dense_1_acc_14: 0.4000 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.2500 - dense_1_acc_19: 0.3000 - dense_1_acc_20: 0.3333 - dense_1_acc_21: 0.2833 - dense_1_acc_22: 0.2167 - dense_1_acc_23: 0.3167 - dense_1_acc_24: 0.2667 - dense_1_acc_25: 0.2333 - dense_1_acc_26: 0.3500 - dense_1_acc_27: 0.3333 - dense_1_acc_28: 0.3667 - dense_1_acc_29: 0.2833 - dense_1_acc_30: 0.0333 Epoch 16/100 60/60 [==============================] - 0s - loss: 71.5976 - dense_1_loss_1: 4.1546 - dense_1_loss_2: 3.7404 - dense_1_loss_3: 3.2748 - dense_1_loss_4: 3.0948 - dense_1_loss_5: 2.8238 - dense_1_loss_6: 2.6795 - dense_1_loss_7: 2.6632 - dense_1_loss_8: 2.3076 - dense_1_loss_9: 2.3430 - dense_1_loss_10: 2.3106 - dense_1_loss_11: 2.2767 - dense_1_loss_12: 2.3521 - dense_1_loss_13: 2.1094 - dense_1_loss_14: 2.0279 - dense_1_loss_15: 2.3001 - dense_1_loss_16: 2.3173 - dense_1_loss_17: 2.1555 - dense_1_loss_18: 2.1540 - dense_1_loss_19: 2.2042 - dense_1_loss_20: 2.2303 - dense_1_loss_21: 2.3217 - dense_1_loss_22: 2.2073 - dense_1_loss_23: 2.2060 - dense_1_loss_24: 2.1873 - dense_1_loss_25: 2.3558 - dense_1_loss_26: 2.1292 - dense_1_loss_27: 2.3397 - dense_1_loss_28: 2.1085 - dense_1_loss_29: 2.2225 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0833 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.2000 - dense_1_acc_7: 0.3000 - dense_1_acc_8: 0.3833 - dense_1_acc_9: 0.5167 - dense_1_acc_10: 0.3667 - dense_1_acc_11: 0.3667 - dense_1_acc_12: 0.3500 - dense_1_acc_13: 0.4000 - dense_1_acc_14: 0.4333 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.3000 - dense_1_acc_18: 0.3500 - dense_1_acc_19: 0.2667 - dense_1_acc_20: 0.3167 - dense_1_acc_21: 0.3000 - dense_1_acc_22: 0.3000 - dense_1_acc_23: 0.4000 - dense_1_acc_24: 0.3167 - dense_1_acc_25: 0.2833 - dense_1_acc_26: 0.4167 - dense_1_acc_27: 0.3833 - dense_1_acc_28: 0.3833 - dense_1_acc_29: 0.2833 - dense_1_acc_30: 0.0167 Epoch 17/100 60/60 [==============================] - 0s - loss: 67.9867 - dense_1_loss_1: 4.1467 - dense_1_loss_2: 3.6946 - dense_1_loss_3: 3.1869 - dense_1_loss_4: 2.9940 - dense_1_loss_5: 2.7163 - dense_1_loss_6: 2.5518 - dense_1_loss_7: 2.5329 - dense_1_loss_8: 2.1870 - dense_1_loss_9: 2.2448 - dense_1_loss_10: 2.2125 - dense_1_loss_11: 2.1790 - dense_1_loss_12: 2.2481 - dense_1_loss_13: 2.0001 - dense_1_loss_14: 1.9775 - dense_1_loss_15: 2.1840 - dense_1_loss_16: 2.1689 - dense_1_loss_17: 2.0737 - dense_1_loss_18: 2.0401 - dense_1_loss_19: 2.1115 - dense_1_loss_20: 2.0538 - dense_1_loss_21: 2.1208 - dense_1_loss_22: 2.0409 - dense_1_loss_23: 2.0493 - dense_1_loss_24: 2.0242 - dense_1_loss_25: 2.1867 - dense_1_loss_26: 1.9568 - dense_1_loss_27: 2.1413 - dense_1_loss_28: 1.9283 - dense_1_loss_29: 2.0341 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2500 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.2833 - dense_1_acc_6: 0.2500 - dense_1_acc_7: 0.3000 - dense_1_acc_8: 0.4000 - dense_1_acc_9: 0.5000 - dense_1_acc_10: 0.3833 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.3333 - dense_1_acc_13: 0.4500 - dense_1_acc_14: 0.3833 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.2833 - dense_1_acc_18: 0.4500 - dense_1_acc_19: 0.3667 - dense_1_acc_20: 0.3500 - dense_1_acc_21: 0.3333 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.4000 - dense_1_acc_24: 0.3833 - dense_1_acc_25: 0.3500 - dense_1_acc_26: 0.4333 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.5167 - dense_1_acc_29: 0.4667 - dense_1_acc_30: 0.0167 Epoch 18/100 60/60 [==============================] - 0s - loss: 64.7090 - dense_1_loss_1: 4.1382 - dense_1_loss_2: 3.6492 - dense_1_loss_3: 3.1033 - dense_1_loss_4: 2.8853 - dense_1_loss_5: 2.6063 - dense_1_loss_6: 2.4070 - dense_1_loss_7: 2.4130 - dense_1_loss_8: 2.0469 - dense_1_loss_9: 2.1290 - dense_1_loss_10: 2.0838 - dense_1_loss_11: 2.0568 - dense_1_loss_12: 2.0717 - dense_1_loss_13: 1.8309 - dense_1_loss_14: 1.8363 - dense_1_loss_15: 2.0575 - dense_1_loss_16: 2.0125 - dense_1_loss_17: 1.9536 - dense_1_loss_18: 1.9070 - dense_1_loss_19: 1.9070 - dense_1_loss_20: 1.8992 - dense_1_loss_21: 1.9849 - dense_1_loss_22: 1.9124 - dense_1_loss_23: 1.9960 - dense_1_loss_24: 1.9664 - dense_1_loss_25: 2.1293 - dense_1_loss_26: 1.8565 - dense_1_loss_27: 2.0799 - dense_1_loss_28: 1.8202 - dense_1_loss_29: 1.9690 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2500 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.2333 - dense_1_acc_7: 0.3667 - dense_1_acc_8: 0.4333 - dense_1_acc_9: 0.4833 - dense_1_acc_10: 0.4167 - dense_1_acc_11: 0.3500 - dense_1_acc_12: 0.4167 - dense_1_acc_13: 0.5167 - dense_1_acc_14: 0.5000 - dense_1_acc_15: 0.3667 - dense_1_acc_16: 0.3667 - dense_1_acc_17: 0.3333 - dense_1_acc_18: 0.4333 - dense_1_acc_19: 0.4000 - dense_1_acc_20: 0.5167 - dense_1_acc_21: 0.4167 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.4167 - dense_1_acc_24: 0.4000 - dense_1_acc_25: 0.3000 - dense_1_acc_26: 0.5000 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.5167 - dense_1_acc_29: 0.4500 - dense_1_acc_30: 0.0167 Epoch 19/100 60/60 [==============================] - 0s - loss: 61.7879 - dense_1_loss_1: 4.1294 - dense_1_loss_2: 3.6042 - dense_1_loss_3: 3.0213 - dense_1_loss_4: 2.7763 - dense_1_loss_5: 2.5027 - dense_1_loss_6: 2.2744 - dense_1_loss_7: 2.2932 - dense_1_loss_8: 1.9236 - dense_1_loss_9: 2.0209 - dense_1_loss_10: 1.9975 - dense_1_loss_11: 1.9088 - dense_1_loss_12: 1.9439 - dense_1_loss_13: 1.7456 - dense_1_loss_14: 1.7787 - dense_1_loss_15: 1.9455 - dense_1_loss_16: 1.9571 - dense_1_loss_17: 1.8686 - dense_1_loss_18: 1.8168 - dense_1_loss_19: 1.8118 - dense_1_loss_20: 1.7946 - dense_1_loss_21: 1.9000 - dense_1_loss_22: 1.8438 - dense_1_loss_23: 1.8485 - dense_1_loss_24: 1.8324 - dense_1_loss_25: 1.9799 - dense_1_loss_26: 1.7386 - dense_1_loss_27: 1.9652 - dense_1_loss_28: 1.7090 - dense_1_loss_29: 1.8555 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3500 - dense_1_acc_7: 0.3500 - dense_1_acc_8: 0.4000 - dense_1_acc_9: 0.4333 - dense_1_acc_10: 0.4000 - dense_1_acc_11: 0.4333 - dense_1_acc_12: 0.3833 - dense_1_acc_13: 0.5500 - dense_1_acc_14: 0.4667 - dense_1_acc_15: 0.4000 - dense_1_acc_16: 0.3500 - dense_1_acc_17: 0.4333 - dense_1_acc_18: 0.4333 - dense_1_acc_19: 0.4833 - dense_1_acc_20: 0.5500 - dense_1_acc_21: 0.3833 - dense_1_acc_22: 0.4167 - dense_1_acc_23: 0.4667 - dense_1_acc_24: 0.3500 - dense_1_acc_25: 0.3500 - dense_1_acc_26: 0.5500 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.6000 - dense_1_acc_29: 0.5000 - dense_1_acc_30: 0.0167 Epoch 20/100 60/60 [==============================] - 0s - loss: 58.9256 - dense_1_loss_1: 4.1216 - dense_1_loss_2: 3.5604 - dense_1_loss_3: 2.9379 - dense_1_loss_4: 2.6596 - dense_1_loss_5: 2.3859 - dense_1_loss_6: 2.1325 - dense_1_loss_7: 2.1609 - dense_1_loss_8: 1.8015 - dense_1_loss_9: 1.9505 - dense_1_loss_10: 1.8831 - dense_1_loss_11: 1.8002 - dense_1_loss_12: 1.7985 - dense_1_loss_13: 1.6470 - dense_1_loss_14: 1.6679 - dense_1_loss_15: 1.8106 - dense_1_loss_16: 1.8684 - dense_1_loss_17: 1.7651 - dense_1_loss_18: 1.6625 - dense_1_loss_19: 1.7251 - dense_1_loss_20: 1.6672 - dense_1_loss_21: 1.8126 - dense_1_loss_22: 1.7288 - dense_1_loss_23: 1.7647 - dense_1_loss_24: 1.6957 - dense_1_loss_25: 1.8690 - dense_1_loss_26: 1.7334 - dense_1_loss_27: 1.8894 - dense_1_loss_28: 1.6947 - dense_1_loss_29: 1.7309 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.3833 - dense_1_acc_7: 0.3500 - dense_1_acc_8: 0.5333 - dense_1_acc_9: 0.3833 - dense_1_acc_10: 0.4667 - dense_1_acc_11: 0.4667 - dense_1_acc_12: 0.4667 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5000 - dense_1_acc_15: 0.4500 - dense_1_acc_16: 0.4000 - dense_1_acc_17: 0.5000 - dense_1_acc_18: 0.5167 - dense_1_acc_19: 0.5667 - dense_1_acc_20: 0.5500 - dense_1_acc_21: 0.4167 - dense_1_acc_22: 0.4167 - dense_1_acc_23: 0.4667 - dense_1_acc_24: 0.5000 - dense_1_acc_25: 0.4000 - dense_1_acc_26: 0.5333 - dense_1_acc_27: 0.4500 - dense_1_acc_28: 0.5500 - dense_1_acc_29: 0.5167 - dense_1_acc_30: 0.0000e+00 Epoch 21/100 60/60 [==============================] - 0s - loss: 55.7586 - dense_1_loss_1: 4.1137 - dense_1_loss_2: 3.5141 - dense_1_loss_3: 2.8520 - dense_1_loss_4: 2.5560 - dense_1_loss_5: 2.2779 - dense_1_loss_6: 1.9934 - dense_1_loss_7: 1.9837 - dense_1_loss_8: 1.7174 - dense_1_loss_9: 1.8271 - dense_1_loss_10: 1.8083 - dense_1_loss_11: 1.6823 - dense_1_loss_12: 1.6463 - dense_1_loss_13: 1.5525 - dense_1_loss_14: 1.5781 - dense_1_loss_15: 1.7534 - dense_1_loss_16: 1.7499 - dense_1_loss_17: 1.6396 - dense_1_loss_18: 1.5731 - dense_1_loss_19: 1.6097 - dense_1_loss_20: 1.5602 - dense_1_loss_21: 1.6816 - dense_1_loss_22: 1.6232 - dense_1_loss_23: 1.6128 - dense_1_loss_24: 1.6132 - dense_1_loss_25: 1.6822 - dense_1_loss_26: 1.5950 - dense_1_loss_27: 1.7374 - dense_1_loss_28: 1.5376 - dense_1_loss_29: 1.6871 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2333 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.4333 - dense_1_acc_7: 0.4333 - dense_1_acc_8: 0.4833 - dense_1_acc_9: 0.4667 - dense_1_acc_10: 0.5333 - dense_1_acc_11: 0.5167 - dense_1_acc_12: 0.4833 - dense_1_acc_13: 0.6833 - dense_1_acc_14: 0.6333 - dense_1_acc_15: 0.4500 - dense_1_acc_16: 0.5000 - dense_1_acc_17: 0.5667 - dense_1_acc_18: 0.5500 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.5000 - dense_1_acc_22: 0.5333 - dense_1_acc_23: 0.6000 - dense_1_acc_24: 0.5667 - dense_1_acc_25: 0.5667 - dense_1_acc_26: 0.5667 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.6500 - dense_1_acc_29: 0.6000 - dense_1_acc_30: 0.0167 Epoch 22/100 60/60 [==============================] - 0s - loss: 53.8419 - dense_1_loss_1: 4.1054 - dense_1_loss_2: 3.4648 - dense_1_loss_3: 2.7634 - dense_1_loss_4: 2.4528 - dense_1_loss_5: 2.1818 - dense_1_loss_6: 1.8950 - dense_1_loss_7: 1.8680 - dense_1_loss_8: 1.6354 - dense_1_loss_9: 1.7240 - dense_1_loss_10: 1.6619 - dense_1_loss_11: 1.6068 - dense_1_loss_12: 1.5566 - dense_1_loss_13: 1.4816 - dense_1_loss_14: 1.4625 - dense_1_loss_15: 1.7267 - dense_1_loss_16: 1.6672 - dense_1_loss_17: 1.6100 - dense_1_loss_18: 1.5442 - dense_1_loss_19: 1.5673 - dense_1_loss_20: 1.5632 - dense_1_loss_21: 1.5932 - dense_1_loss_22: 1.5911 - dense_1_loss_23: 1.5904 - dense_1_loss_24: 1.5194 - dense_1_loss_25: 1.6588 - dense_1_loss_26: 1.5457 - dense_1_loss_27: 1.6742 - dense_1_loss_28: 1.5298 - dense_1_loss_29: 1.6005 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3833 - dense_1_acc_6: 0.4167 - dense_1_acc_7: 0.4833 - dense_1_acc_8: 0.5667 - dense_1_acc_9: 0.5833 - dense_1_acc_10: 0.6000 - dense_1_acc_11: 0.5667 - dense_1_acc_12: 0.5333 - dense_1_acc_13: 0.6333 - dense_1_acc_14: 0.6333 - dense_1_acc_15: 0.5000 - dense_1_acc_16: 0.4667 - dense_1_acc_17: 0.4833 - dense_1_acc_18: 0.5833 - dense_1_acc_19: 0.6167 - dense_1_acc_20: 0.6000 - dense_1_acc_21: 0.5000 - dense_1_acc_22: 0.5833 - dense_1_acc_23: 0.6167 - dense_1_acc_24: 0.6333 - dense_1_acc_25: 0.5333 - dense_1_acc_26: 0.6167 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.6667 - dense_1_acc_29: 0.7500 - dense_1_acc_30: 0.0000e+00 Epoch 23/100 60/60 [==============================] - 0s - loss: 52.5639 - dense_1_loss_1: 4.0970 - dense_1_loss_2: 3.4171 - dense_1_loss_3: 2.6765 - dense_1_loss_4: 2.3563 - dense_1_loss_5: 2.0844 - dense_1_loss_6: 1.8203 - dense_1_loss_7: 1.7338 - dense_1_loss_8: 1.5532 - dense_1_loss_9: 1.6951 - dense_1_loss_10: 1.6518 - dense_1_loss_11: 1.6281 - dense_1_loss_12: 1.5193 - dense_1_loss_13: 1.4603 - dense_1_loss_14: 1.4460 - dense_1_loss_15: 1.6846 - dense_1_loss_16: 1.6818 - dense_1_loss_17: 1.5478 - dense_1_loss_18: 1.4713 - dense_1_loss_19: 1.4925 - dense_1_loss_20: 1.4830 - dense_1_loss_21: 1.5245 - dense_1_loss_22: 1.5590 - dense_1_loss_23: 1.5870 - dense_1_loss_24: 1.4994 - dense_1_loss_25: 1.6633 - dense_1_loss_26: 1.5232 - dense_1_loss_27: 1.6497 - dense_1_loss_28: 1.4470 - dense_1_loss_29: 1.6107 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.4333 - dense_1_acc_6: 0.4500 - dense_1_acc_7: 0.5000 - dense_1_acc_8: 0.6167 - dense_1_acc_9: 0.6000 - dense_1_acc_10: 0.5167 - dense_1_acc_11: 0.5500 - dense_1_acc_12: 0.5333 - dense_1_acc_13: 0.6500 - dense_1_acc_14: 0.6167 - dense_1_acc_15: 0.5000 - dense_1_acc_16: 0.5000 - dense_1_acc_17: 0.5500 - dense_1_acc_18: 0.6000 - dense_1_acc_19: 0.6000 - dense_1_acc_20: 0.5667 - dense_1_acc_21: 0.6000 - dense_1_acc_22: 0.5333 - dense_1_acc_23: 0.6667 - dense_1_acc_24: 0.5500 - dense_1_acc_25: 0.5500 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.5667 - dense_1_acc_28: 0.6333 - dense_1_acc_29: 0.6500 - dense_1_acc_30: 0.0167 Epoch 24/100 60/60 [==============================] - 0s - loss: 50.3327 - dense_1_loss_1: 4.0893 - dense_1_loss_2: 3.3710 - dense_1_loss_3: 2.6004 - dense_1_loss_4: 2.2775 - dense_1_loss_5: 2.0196 - dense_1_loss_6: 1.7407 - dense_1_loss_7: 1.6348 - dense_1_loss_8: 1.4760 - dense_1_loss_9: 1.5477 - dense_1_loss_10: 1.5480 - dense_1_loss_11: 1.5433 - dense_1_loss_12: 1.4391 - dense_1_loss_13: 1.3694 - dense_1_loss_14: 1.4356 - dense_1_loss_15: 1.5174 - dense_1_loss_16: 1.5525 - dense_1_loss_17: 1.4400 - dense_1_loss_18: 1.4194 - dense_1_loss_19: 1.5160 - dense_1_loss_20: 1.3971 - dense_1_loss_21: 1.4767 - dense_1_loss_22: 1.4980 - dense_1_loss_23: 1.4167 - dense_1_loss_24: 1.4184 - dense_1_loss_25: 1.4994 - dense_1_loss_26: 1.5017 - dense_1_loss_27: 1.6044 - dense_1_loss_28: 1.5110 - dense_1_loss_29: 1.4716 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4833 - dense_1_acc_4: 0.3167 - dense_1_acc_5: 0.4500 - dense_1_acc_6: 0.5500 - dense_1_acc_7: 0.6000 - dense_1_acc_8: 0.6333 - dense_1_acc_9: 0.6167 - dense_1_acc_10: 0.5667 - dense_1_acc_11: 0.5333 - dense_1_acc_12: 0.6167 - dense_1_acc_13: 0.6833 - dense_1_acc_14: 0.6500 - dense_1_acc_15: 0.5833 - dense_1_acc_16: 0.5833 - dense_1_acc_17: 0.6667 - dense_1_acc_18: 0.6333 - dense_1_acc_19: 0.6000 - dense_1_acc_20: 0.7167 - dense_1_acc_21: 0.5667 - dense_1_acc_22: 0.6000 - dense_1_acc_23: 0.6167 - dense_1_acc_24: 0.6000 - dense_1_acc_25: 0.5667 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.5833 - dense_1_acc_28: 0.7167 - dense_1_acc_29: 0.7833 - dense_1_acc_30: 0.0167 Epoch 25/100 60/60 [==============================] - 0s - loss: 47.0178 - dense_1_loss_1: 4.0811 - dense_1_loss_2: 3.3284 - dense_1_loss_3: 2.5237 - dense_1_loss_4: 2.1889 - dense_1_loss_5: 1.9207 - dense_1_loss_6: 1.6142 - dense_1_loss_7: 1.5375 - dense_1_loss_8: 1.4322 - dense_1_loss_9: 1.4851 - dense_1_loss_10: 1.4718 - dense_1_loss_11: 1.4493 - dense_1_loss_12: 1.3042 - dense_1_loss_13: 1.2375 - dense_1_loss_14: 1.2453 - dense_1_loss_15: 1.4302 - dense_1_loss_16: 1.4631 - dense_1_loss_17: 1.3381 - dense_1_loss_18: 1.2717 - dense_1_loss_19: 1.2964 - dense_1_loss_20: 1.2995 - dense_1_loss_21: 1.3096 - dense_1_loss_22: 1.3586 - dense_1_loss_23: 1.3515 - dense_1_loss_24: 1.2906 - dense_1_loss_25: 1.3689 - dense_1_loss_26: 1.2869 - dense_1_loss_27: 1.4289 - dense_1_loss_28: 1.3491 - dense_1_loss_29: 1.3549 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3500 - dense_1_acc_5: 0.4667 - dense_1_acc_6: 0.5167 - dense_1_acc_7: 0.5167 - dense_1_acc_8: 0.6167 - dense_1_acc_9: 0.6333 - dense_1_acc_10: 0.6333 - dense_1_acc_11: 0.6333 - dense_1_acc_12: 0.7167 - dense_1_acc_13: 0.7333 - dense_1_acc_14: 0.8167 - dense_1_acc_15: 0.5833 - dense_1_acc_16: 0.6000 - dense_1_acc_17: 0.7667 - dense_1_acc_18: 0.6833 - dense_1_acc_19: 0.6667 - dense_1_acc_20: 0.7167 - dense_1_acc_21: 0.7500 - dense_1_acc_22: 0.6833 - dense_1_acc_23: 0.7667 - dense_1_acc_24: 0.7833 - dense_1_acc_25: 0.6667 - dense_1_acc_26: 0.8000 - dense_1_acc_27: 0.6333 - dense_1_acc_28: 0.8333 - dense_1_acc_29: 0.8000 - dense_1_acc_30: 0.0167 Epoch 26/100 60/60 [==============================] - 0s - loss: 45.4393 - dense_1_loss_1: 4.0740 - dense_1_loss_2: 3.2820 - dense_1_loss_3: 2.4405 - dense_1_loss_4: 2.1176 - dense_1_loss_5: 1.8400 - dense_1_loss_6: 1.5200 - dense_1_loss_7: 1.4888 - dense_1_loss_8: 1.3896 - dense_1_loss_9: 1.4525 - dense_1_loss_10: 1.4158 - dense_1_loss_11: 1.4071 - dense_1_loss_12: 1.2865 - dense_1_loss_13: 1.2423 - dense_1_loss_14: 1.2398 - dense_1_loss_15: 1.3612 - dense_1_loss_16: 1.3998 - dense_1_loss_17: 1.2446 - dense_1_loss_18: 1.2307 - dense_1_loss_19: 1.2580 - dense_1_loss_20: 1.2107 - dense_1_loss_21: 1.2617 - dense_1_loss_22: 1.3165 - dense_1_loss_23: 1.2981 - dense_1_loss_24: 1.1829 - dense_1_loss_25: 1.3127 - dense_1_loss_26: 1.2216 - dense_1_loss_27: 1.3485 - dense_1_loss_28: 1.2942 - dense_1_loss_29: 1.3016 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3167 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3667 - dense_1_acc_5: 0.5000 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.6167 - dense_1_acc_8: 0.6333 - dense_1_acc_9: 0.6333 - dense_1_acc_10: 0.6500 - dense_1_acc_11: 0.6333 - dense_1_acc_12: 0.7667 - dense_1_acc_13: 0.7167 - dense_1_acc_14: 0.7667 - dense_1_acc_15: 0.6667 - dense_1_acc_16: 0.6833 - dense_1_acc_17: 0.8000 - dense_1_acc_18: 0.7000 - dense_1_acc_19: 0.6667 - dense_1_acc_20: 0.7333 - dense_1_acc_21: 0.6667 - dense_1_acc_22: 0.6333 - dense_1_acc_23: 0.7000 - dense_1_acc_24: 0.8167 - dense_1_acc_25: 0.6667 - dense_1_acc_26: 0.8667 - dense_1_acc_27: 0.6333 - dense_1_acc_28: 0.8000 - dense_1_acc_29: 0.7667 - dense_1_acc_30: 0.0167 Epoch 27/100 60/60 [==============================] - 0s - loss: 42.6041 - dense_1_loss_1: 4.0658 - dense_1_loss_2: 3.2344 - dense_1_loss_3: 2.3644 - dense_1_loss_4: 2.0530 - dense_1_loss_5: 1.7510 - dense_1_loss_6: 1.4306 - dense_1_loss_7: 1.3784 - dense_1_loss_8: 1.2999 - dense_1_loss_9: 1.2461 - dense_1_loss_10: 1.2743 - dense_1_loss_11: 1.2196 - dense_1_loss_12: 1.1600 - dense_1_loss_13: 1.0798 - dense_1_loss_14: 1.1287 - dense_1_loss_15: 1.2459 - dense_1_loss_16: 1.2929 - dense_1_loss_17: 1.1157 - dense_1_loss_18: 1.1411 - dense_1_loss_19: 1.2040 - dense_1_loss_20: 1.1237 - dense_1_loss_21: 1.1996 - dense_1_loss_22: 1.2165 - dense_1_loss_23: 1.1386 - dense_1_loss_24: 1.1431 - dense_1_loss_25: 1.2137 - dense_1_loss_26: 1.1745 - dense_1_loss_27: 1.2993 - dense_1_loss_28: 1.1910 - dense_1_loss_29: 1.2187 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.4667 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.5333 - dense_1_acc_6: 0.6167 - dense_1_acc_7: 0.6167 - dense_1_acc_8: 0.7000 - dense_1_acc_9: 0.7333 - dense_1_acc_10: 0.6833 - dense_1_acc_11: 0.7333 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.8000 - dense_1_acc_14: 0.7833 - dense_1_acc_15: 0.6667 - dense_1_acc_16: 0.7167 - dense_1_acc_17: 0.8500 - dense_1_acc_18: 0.7667 - dense_1_acc_19: 0.6833 - dense_1_acc_20: 0.8833 - dense_1_acc_21: 0.7667 - dense_1_acc_22: 0.6333 - dense_1_acc_23: 0.8000 - dense_1_acc_24: 0.7167 - dense_1_acc_25: 0.7333 - dense_1_acc_26: 0.8167 - dense_1_acc_27: 0.6167 - dense_1_acc_28: 0.7833 - dense_1_acc_29: 0.8333 - dense_1_acc_30: 0.0167 Epoch 28/100 60/60 [==============================] - 0s - loss: 40.5655 - dense_1_loss_1: 4.0579 - dense_1_loss_2: 3.1859 - dense_1_loss_3: 2.2873 - dense_1_loss_4: 1.9731 - dense_1_loss_5: 1.6698 - dense_1_loss_6: 1.3399 - dense_1_loss_7: 1.2858 - dense_1_loss_8: 1.2127 - dense_1_loss_9: 1.1445 - dense_1_loss_10: 1.1579 - dense_1_loss_11: 1.0985 - dense_1_loss_12: 1.0865 - dense_1_loss_13: 1.0140 - dense_1_loss_14: 1.0523 - dense_1_loss_15: 1.1817 - dense_1_loss_16: 1.2035 - dense_1_loss_17: 1.0582 - dense_1_loss_18: 1.0634 - dense_1_loss_19: 1.1452 - dense_1_loss_20: 1.0872 - dense_1_loss_21: 1.1761 - dense_1_loss_22: 1.1338 - dense_1_loss_23: 1.0867 - dense_1_loss_24: 1.0887 - dense_1_loss_25: 1.1328 - dense_1_loss_26: 1.1551 - dense_1_loss_27: 1.2105 - dense_1_loss_28: 1.1166 - dense_1_loss_29: 1.1599 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5167 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.5500 - dense_1_acc_6: 0.7167 - dense_1_acc_7: 0.7833 - dense_1_acc_8: 0.7333 - dense_1_acc_9: 0.7833 - dense_1_acc_10: 0.7667 - dense_1_acc_11: 0.8167 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.7833 - dense_1_acc_14: 0.8000 - dense_1_acc_15: 0.6500 - dense_1_acc_16: 0.7667 - dense_1_acc_17: 0.8333 - dense_1_acc_18: 0.8667 - dense_1_acc_19: 0.7833 - dense_1_acc_20: 0.9167 - dense_1_acc_21: 0.7833 - dense_1_acc_22: 0.7333 - dense_1_acc_23: 0.8167 - dense_1_acc_24: 0.8000 - dense_1_acc_25: 0.7333 - dense_1_acc_26: 0.7667 - dense_1_acc_27: 0.6667 - dense_1_acc_28: 0.7833 - dense_1_acc_29: 0.8167 - dense_1_acc_30: 0.0167 Epoch 29/100 60/60 [==============================] - 0s - loss: 38.2237 - dense_1_loss_1: 4.0502 - dense_1_loss_2: 3.1388 - dense_1_loss_3: 2.2188 - dense_1_loss_4: 1.8866 - dense_1_loss_5: 1.5620 - dense_1_loss_6: 1.2492 - dense_1_loss_7: 1.1946 - dense_1_loss_8: 1.1205 - dense_1_loss_9: 1.1170 - dense_1_loss_10: 1.0810 - dense_1_loss_11: 1.0287 - dense_1_loss_12: 0.9828 - dense_1_loss_13: 0.9248 - dense_1_loss_14: 0.9624 - dense_1_loss_15: 1.1211 - dense_1_loss_16: 1.0972 - dense_1_loss_17: 0.9937 - dense_1_loss_18: 0.9918 - dense_1_loss_19: 1.0248 - dense_1_loss_20: 1.0295 - dense_1_loss_21: 1.0935 - dense_1_loss_22: 1.0346 - dense_1_loss_23: 1.0280 - dense_1_loss_24: 0.9644 - dense_1_loss_25: 1.0882 - dense_1_loss_26: 1.0549 - dense_1_loss_27: 1.0832 - dense_1_loss_28: 1.0110 - dense_1_loss_29: 1.0900 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5167 - dense_1_acc_4: 0.4000 - dense_1_acc_5: 0.6167 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.7833 - dense_1_acc_8: 0.8500 - dense_1_acc_9: 0.8500 - dense_1_acc_10: 0.8000 - dense_1_acc_11: 0.8333 - dense_1_acc_12: 0.9000 - dense_1_acc_13: 0.9167 - dense_1_acc_14: 0.8500 - dense_1_acc_15: 0.7167 - dense_1_acc_16: 0.8000 - dense_1_acc_17: 0.8833 - dense_1_acc_18: 0.8667 - dense_1_acc_19: 0.8833 - dense_1_acc_20: 0.9333 - dense_1_acc_21: 0.8333 - dense_1_acc_22: 0.8500 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.9167 - dense_1_acc_25: 0.7833 - dense_1_acc_26: 0.8500 - dense_1_acc_27: 0.7833 - dense_1_acc_28: 0.9000 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0167 Epoch 30/100 60/60 [==============================] - 0s - loss: 36.3616 - dense_1_loss_1: 4.0424 - dense_1_loss_2: 3.0884 - dense_1_loss_3: 2.1510 - dense_1_loss_4: 1.8009 - dense_1_loss_5: 1.4770 - dense_1_loss_6: 1.1533 - dense_1_loss_7: 1.1274 - dense_1_loss_8: 1.0581 - dense_1_loss_9: 1.0600 - dense_1_loss_10: 1.0173 - dense_1_loss_11: 0.9440 - dense_1_loss_12: 0.9038 - dense_1_loss_13: 0.8767 - dense_1_loss_14: 0.8886 - dense_1_loss_15: 1.0600 - dense_1_loss_16: 0.9996 - dense_1_loss_17: 0.9649 - dense_1_loss_18: 0.9262 - dense_1_loss_19: 0.9161 - dense_1_loss_20: 0.9624 - dense_1_loss_21: 1.0519 - dense_1_loss_22: 0.9694 - dense_1_loss_23: 0.9694 - dense_1_loss_24: 0.9009 - dense_1_loss_25: 1.0462 - dense_1_loss_26: 0.9700 - dense_1_loss_27: 1.0122 - dense_1_loss_28: 0.9812 - dense_1_loss_29: 1.0421 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5167 - dense_1_acc_4: 0.4333 - dense_1_acc_5: 0.6500 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.7833 - dense_1_acc_8: 0.8667 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.8333 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.8833 - dense_1_acc_14: 0.9333 - dense_1_acc_15: 0.8000 - dense_1_acc_16: 0.9333 - dense_1_acc_17: 0.9000 - dense_1_acc_18: 0.8667 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.8333 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.7667 - dense_1_acc_26: 0.8833 - dense_1_acc_27: 0.8000 - dense_1_acc_28: 0.9167 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0167 Epoch 31/100 60/60 [==============================] - 0s - loss: 34.4244 - dense_1_loss_1: 4.0337 - dense_1_loss_2: 3.0397 - dense_1_loss_3: 2.0881 - dense_1_loss_4: 1.7215 - dense_1_loss_5: 1.3989 - dense_1_loss_6: 1.0827 - dense_1_loss_7: 1.0739 - dense_1_loss_8: 0.9919 - dense_1_loss_9: 0.9861 - dense_1_loss_10: 0.9402 - dense_1_loss_11: 0.8850 - dense_1_loss_12: 0.8405 - dense_1_loss_13: 0.8069 - dense_1_loss_14: 0.8318 - dense_1_loss_15: 0.9706 - dense_1_loss_16: 0.9393 - dense_1_loss_17: 0.8828 - dense_1_loss_18: 0.8541 - dense_1_loss_19: 0.8478 - dense_1_loss_20: 0.8710 - dense_1_loss_21: 0.9757 - dense_1_loss_22: 0.9120 - dense_1_loss_23: 0.8761 - dense_1_loss_24: 0.8576 - dense_1_loss_25: 0.9691 - dense_1_loss_26: 0.8863 - dense_1_loss_27: 0.9430 - dense_1_loss_28: 0.9314 - dense_1_loss_29: 0.9867 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5333 - dense_1_acc_4: 0.4333 - dense_1_acc_5: 0.6833 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.8667 - dense_1_acc_8: 0.8833 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.8333 - dense_1_acc_11: 0.8667 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9333 - dense_1_acc_15: 0.8833 - dense_1_acc_16: 0.9333 - dense_1_acc_17: 0.9333 - dense_1_acc_18: 0.9333 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9333 - dense_1_acc_21: 0.8833 - dense_1_acc_22: 0.8500 - dense_1_acc_23: 0.9333 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.8167 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.8333 - dense_1_acc_28: 0.9333 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0167 Epoch 32/100 60/60 [==============================] - 0s - loss: 32.6345 - dense_1_loss_1: 4.0258 - dense_1_loss_2: 2.9908 - dense_1_loss_3: 2.0231 - dense_1_loss_4: 1.6501 - dense_1_loss_5: 1.3245 - dense_1_loss_6: 1.0153 - dense_1_loss_7: 1.0165 - dense_1_loss_8: 0.9249 - dense_1_loss_9: 0.9085 - dense_1_loss_10: 0.8572 - dense_1_loss_11: 0.8254 - dense_1_loss_12: 0.8031 - dense_1_loss_13: 0.7358 - dense_1_loss_14: 0.7810 - dense_1_loss_15: 0.8953 - dense_1_loss_16: 0.8728 - dense_1_loss_17: 0.8122 - dense_1_loss_18: 0.7989 - dense_1_loss_19: 0.8107 - dense_1_loss_20: 0.8104 - dense_1_loss_21: 0.9180 - dense_1_loss_22: 0.8462 - dense_1_loss_23: 0.7901 - dense_1_loss_24: 0.8070 - dense_1_loss_25: 0.8798 - dense_1_loss_26: 0.8097 - dense_1_loss_27: 0.8894 - dense_1_loss_28: 0.8827 - dense_1_loss_29: 0.9292 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.5333 - dense_1_acc_4: 0.4333 - dense_1_acc_5: 0.7000 - dense_1_acc_6: 0.8333 - dense_1_acc_7: 0.8667 - dense_1_acc_8: 0.8833 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.8667 - dense_1_acc_11: 0.8667 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.9667 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.9167 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9167 - dense_1_acc_22: 0.9333 - dense_1_acc_23: 0.9667 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.8167 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.8833 - dense_1_acc_28: 0.9333 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0167 Epoch 33/100 60/60 [==============================] - 0s - loss: 30.9389 - dense_1_loss_1: 4.0188 - dense_1_loss_2: 2.9407 - dense_1_loss_3: 1.9521 - dense_1_loss_4: 1.5795 - dense_1_loss_5: 1.2516 - dense_1_loss_6: 0.9450 - dense_1_loss_7: 0.9530 - dense_1_loss_8: 0.8681 - dense_1_loss_9: 0.8322 - dense_1_loss_10: 0.7903 - dense_1_loss_11: 0.7426 - dense_1_loss_12: 0.7353 - dense_1_loss_13: 0.6751 - dense_1_loss_14: 0.7124 - dense_1_loss_15: 0.8422 - dense_1_loss_16: 0.8000 - dense_1_loss_17: 0.7533 - dense_1_loss_18: 0.7463 - dense_1_loss_19: 0.7683 - dense_1_loss_20: 0.7605 - dense_1_loss_21: 0.8494 - dense_1_loss_22: 0.7875 - dense_1_loss_23: 0.7459 - dense_1_loss_24: 0.7640 - dense_1_loss_25: 0.8170 - dense_1_loss_26: 0.7658 - dense_1_loss_27: 0.8427 - dense_1_loss_28: 0.8281 - dense_1_loss_29: 0.8710 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.5667 - dense_1_acc_4: 0.4667 - dense_1_acc_5: 0.7167 - dense_1_acc_6: 0.8500 - dense_1_acc_7: 0.9000 - dense_1_acc_8: 0.8833 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 0.8833 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9333 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0167 Epoch 34/100 60/60 [==============================] - 0s - loss: 29.2992 - dense_1_loss_1: 4.0114 - dense_1_loss_2: 2.8959 - dense_1_loss_3: 1.8887 - dense_1_loss_4: 1.5026 - dense_1_loss_5: 1.1774 - dense_1_loss_6: 0.8902 - dense_1_loss_7: 0.8931 - dense_1_loss_8: 0.8067 - dense_1_loss_9: 0.7825 - dense_1_loss_10: 0.7301 - dense_1_loss_11: 0.6971 - dense_1_loss_12: 0.6586 - dense_1_loss_13: 0.6264 - dense_1_loss_14: 0.6714 - dense_1_loss_15: 0.7694 - dense_1_loss_16: 0.7253 - dense_1_loss_17: 0.7126 - dense_1_loss_18: 0.6899 - dense_1_loss_19: 0.7302 - dense_1_loss_20: 0.7292 - dense_1_loss_21: 0.7671 - dense_1_loss_22: 0.7022 - dense_1_loss_23: 0.7018 - dense_1_loss_24: 0.6975 - dense_1_loss_25: 0.7682 - dense_1_loss_26: 0.7127 - dense_1_loss_27: 0.7716 - dense_1_loss_28: 0.7787 - dense_1_loss_29: 0.8106 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.5167 - dense_1_acc_5: 0.7167 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9167 - dense_1_acc_8: 0.8833 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 0.9000 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 0.9833 - dense_1_acc_19: 0.9333 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 0.9833 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9667 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 35/100 60/60 [==============================] - 0s - loss: 27.8295 - dense_1_loss_1: 4.0044 - dense_1_loss_2: 2.8496 - dense_1_loss_3: 1.8249 - dense_1_loss_4: 1.4217 - dense_1_loss_5: 1.1137 - dense_1_loss_6: 0.8429 - dense_1_loss_7: 0.8338 - dense_1_loss_8: 0.7510 - dense_1_loss_9: 0.7374 - dense_1_loss_10: 0.6947 - dense_1_loss_11: 0.6563 - dense_1_loss_12: 0.6021 - dense_1_loss_13: 0.5791 - dense_1_loss_14: 0.6385 - dense_1_loss_15: 0.7006 - dense_1_loss_16: 0.6749 - dense_1_loss_17: 0.6686 - dense_1_loss_18: 0.6332 - dense_1_loss_19: 0.6830 - dense_1_loss_20: 0.6814 - dense_1_loss_21: 0.7074 - dense_1_loss_22: 0.6550 - dense_1_loss_23: 0.6576 - dense_1_loss_24: 0.6438 - dense_1_loss_25: 0.7175 - dense_1_loss_26: 0.6573 - dense_1_loss_27: 0.7081 - dense_1_loss_28: 0.7247 - dense_1_loss_29: 0.7663 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.5667 - dense_1_acc_4: 0.5500 - dense_1_acc_5: 0.7333 - dense_1_acc_6: 0.9000 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.8833 - dense_1_acc_9: 0.9167 - dense_1_acc_10: 0.9167 - dense_1_acc_11: 0.9000 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 0.9833 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 36/100 60/60 [==============================] - 0s - loss: 26.3358 - dense_1_loss_1: 3.9986 - dense_1_loss_2: 2.8044 - dense_1_loss_3: 1.7646 - dense_1_loss_4: 1.3426 - dense_1_loss_5: 1.0514 - dense_1_loss_6: 0.7839 - dense_1_loss_7: 0.7844 - dense_1_loss_8: 0.7047 - dense_1_loss_9: 0.6619 - dense_1_loss_10: 0.6508 - dense_1_loss_11: 0.5935 - dense_1_loss_12: 0.5552 - dense_1_loss_13: 0.5298 - dense_1_loss_14: 0.5928 - dense_1_loss_15: 0.6541 - dense_1_loss_16: 0.6278 - dense_1_loss_17: 0.6162 - dense_1_loss_18: 0.5921 - dense_1_loss_19: 0.6097 - dense_1_loss_20: 0.6208 - dense_1_loss_21: 0.6831 - dense_1_loss_22: 0.6093 - dense_1_loss_23: 0.6029 - dense_1_loss_24: 0.5956 - dense_1_loss_25: 0.6530 - dense_1_loss_26: 0.6171 - dense_1_loss_27: 0.6466 - dense_1_loss_28: 0.6642 - dense_1_loss_29: 0.7248 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.5833 - dense_1_acc_4: 0.5667 - dense_1_acc_5: 0.7500 - dense_1_acc_6: 0.9000 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.8667 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.9500 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 37/100 60/60 [==============================] - 0s - loss: 24.9947 - dense_1_loss_1: 3.9924 - dense_1_loss_2: 2.7588 - dense_1_loss_3: 1.7069 - dense_1_loss_4: 1.2710 - dense_1_loss_5: 0.9867 - dense_1_loss_6: 0.7383 - dense_1_loss_7: 0.7390 - dense_1_loss_8: 0.6571 - dense_1_loss_9: 0.6146 - dense_1_loss_10: 0.5959 - dense_1_loss_11: 0.5446 - dense_1_loss_12: 0.5211 - dense_1_loss_13: 0.4819 - dense_1_loss_14: 0.5398 - dense_1_loss_15: 0.5999 - dense_1_loss_16: 0.5843 - dense_1_loss_17: 0.5631 - dense_1_loss_18: 0.5511 - dense_1_loss_19: 0.5566 - dense_1_loss_20: 0.5763 - dense_1_loss_21: 0.6558 - dense_1_loss_22: 0.5703 - dense_1_loss_23: 0.5537 - dense_1_loss_24: 0.5513 - dense_1_loss_25: 0.5992 - dense_1_loss_26: 0.5884 - dense_1_loss_27: 0.5942 - dense_1_loss_28: 0.6187 - dense_1_loss_29: 0.6837 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.6333 - dense_1_acc_5: 0.7833 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.9000 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 38/100 60/60 [==============================] - 0s - loss: 23.7322 - dense_1_loss_1: 3.9864 - dense_1_loss_2: 2.7148 - dense_1_loss_3: 1.6553 - dense_1_loss_4: 1.2017 - dense_1_loss_5: 0.9246 - dense_1_loss_6: 0.6939 - dense_1_loss_7: 0.6976 - dense_1_loss_8: 0.6109 - dense_1_loss_9: 0.5861 - dense_1_loss_10: 0.5381 - dense_1_loss_11: 0.5088 - dense_1_loss_12: 0.4805 - dense_1_loss_13: 0.4441 - dense_1_loss_14: 0.4932 - dense_1_loss_15: 0.5420 - dense_1_loss_16: 0.5378 - dense_1_loss_17: 0.5212 - dense_1_loss_18: 0.5162 - dense_1_loss_19: 0.5124 - dense_1_loss_20: 0.5408 - dense_1_loss_21: 0.6015 - dense_1_loss_22: 0.5307 - dense_1_loss_23: 0.5154 - dense_1_loss_24: 0.4961 - dense_1_loss_25: 0.5636 - dense_1_loss_26: 0.5542 - dense_1_loss_27: 0.5521 - dense_1_loss_28: 0.5815 - dense_1_loss_29: 0.6307 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.6667 - dense_1_acc_5: 0.8000 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.9333 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9500 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 39/100 60/60 [==============================] - 0s - loss: 22.5507 - dense_1_loss_1: 3.9812 - dense_1_loss_2: 2.6694 - dense_1_loss_3: 1.6006 - dense_1_loss_4: 1.1395 - dense_1_loss_5: 0.8662 - dense_1_loss_6: 0.6477 - dense_1_loss_7: 0.6601 - dense_1_loss_8: 0.5685 - dense_1_loss_9: 0.5482 - dense_1_loss_10: 0.4969 - dense_1_loss_11: 0.4673 - dense_1_loss_12: 0.4430 - dense_1_loss_13: 0.4054 - dense_1_loss_14: 0.4577 - dense_1_loss_15: 0.5099 - dense_1_loss_16: 0.4892 - dense_1_loss_17: 0.4865 - dense_1_loss_18: 0.4777 - dense_1_loss_19: 0.4845 - dense_1_loss_20: 0.5019 - dense_1_loss_21: 0.5481 - dense_1_loss_22: 0.5029 - dense_1_loss_23: 0.4861 - dense_1_loss_24: 0.4468 - dense_1_loss_25: 0.5225 - dense_1_loss_26: 0.5088 - dense_1_loss_27: 0.5114 - dense_1_loss_28: 0.5357 - dense_1_loss_29: 0.5870 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7167 - dense_1_acc_5: 0.8000 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 40/100 60/60 [==============================] - 0s - loss: 21.4203 - dense_1_loss_1: 3.9757 - dense_1_loss_2: 2.6254 - dense_1_loss_3: 1.5507 - dense_1_loss_4: 1.0730 - dense_1_loss_5: 0.8163 - dense_1_loss_6: 0.5963 - dense_1_loss_7: 0.6166 - dense_1_loss_8: 0.5254 - dense_1_loss_9: 0.5073 - dense_1_loss_10: 0.4499 - dense_1_loss_11: 0.4294 - dense_1_loss_12: 0.4058 - dense_1_loss_13: 0.3753 - dense_1_loss_14: 0.4193 - dense_1_loss_15: 0.4856 - dense_1_loss_16: 0.4481 - dense_1_loss_17: 0.4511 - dense_1_loss_18: 0.4381 - dense_1_loss_19: 0.4513 - dense_1_loss_20: 0.4676 - dense_1_loss_21: 0.5130 - dense_1_loss_22: 0.4637 - dense_1_loss_23: 0.4525 - dense_1_loss_24: 0.4153 - dense_1_loss_25: 0.4829 - dense_1_loss_26: 0.4582 - dense_1_loss_27: 0.4751 - dense_1_loss_28: 0.4935 - dense_1_loss_29: 0.5579 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7167 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 41/100 60/60 [==============================] - 0s - loss: 20.3717 - dense_1_loss_1: 3.9704 - dense_1_loss_2: 2.5801 - dense_1_loss_3: 1.4991 - dense_1_loss_4: 1.0112 - dense_1_loss_5: 0.7674 - dense_1_loss_6: 0.5590 - dense_1_loss_7: 0.5775 - dense_1_loss_8: 0.4876 - dense_1_loss_9: 0.4741 - dense_1_loss_10: 0.4162 - dense_1_loss_11: 0.3986 - dense_1_loss_12: 0.3672 - dense_1_loss_13: 0.3480 - dense_1_loss_14: 0.3927 - dense_1_loss_15: 0.4469 - dense_1_loss_16: 0.4110 - dense_1_loss_17: 0.4195 - dense_1_loss_18: 0.3960 - dense_1_loss_19: 0.4163 - dense_1_loss_20: 0.4371 - dense_1_loss_21: 0.4735 - dense_1_loss_22: 0.4258 - dense_1_loss_23: 0.4193 - dense_1_loss_24: 0.3882 - dense_1_loss_25: 0.4473 - dense_1_loss_26: 0.4157 - dense_1_loss_27: 0.4332 - dense_1_loss_28: 0.4633 - dense_1_loss_29: 0.5295 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7333 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 42/100 60/60 [==============================] - 0s - loss: 19.3898 - dense_1_loss_1: 3.9644 - dense_1_loss_2: 2.5403 - dense_1_loss_3: 1.4491 - dense_1_loss_4: 0.9557 - dense_1_loss_5: 0.7225 - dense_1_loss_6: 0.5234 - dense_1_loss_7: 0.5418 - dense_1_loss_8: 0.4551 - dense_1_loss_9: 0.4443 - dense_1_loss_10: 0.3866 - dense_1_loss_11: 0.3695 - dense_1_loss_12: 0.3353 - dense_1_loss_13: 0.3195 - dense_1_loss_14: 0.3667 - dense_1_loss_15: 0.4077 - dense_1_loss_16: 0.3799 - dense_1_loss_17: 0.3883 - dense_1_loss_18: 0.3652 - dense_1_loss_19: 0.3865 - dense_1_loss_20: 0.4014 - dense_1_loss_21: 0.4333 - dense_1_loss_22: 0.3932 - dense_1_loss_23: 0.3857 - dense_1_loss_24: 0.3620 - dense_1_loss_25: 0.4089 - dense_1_loss_26: 0.3793 - dense_1_loss_27: 0.3934 - dense_1_loss_28: 0.4367 - dense_1_loss_29: 0.4941 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.8667 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 43/100 60/60 [==============================] - 0s - loss: 18.4915 - dense_1_loss_1: 3.9594 - dense_1_loss_2: 2.4960 - dense_1_loss_3: 1.3996 - dense_1_loss_4: 0.9024 - dense_1_loss_5: 0.6782 - dense_1_loss_6: 0.4897 - dense_1_loss_7: 0.5067 - dense_1_loss_8: 0.4250 - dense_1_loss_9: 0.4125 - dense_1_loss_10: 0.3601 - dense_1_loss_11: 0.3371 - dense_1_loss_12: 0.3142 - dense_1_loss_13: 0.2942 - dense_1_loss_14: 0.3416 - dense_1_loss_15: 0.3710 - dense_1_loss_16: 0.3487 - dense_1_loss_17: 0.3592 - dense_1_loss_18: 0.3427 - dense_1_loss_19: 0.3569 - dense_1_loss_20: 0.3676 - dense_1_loss_21: 0.4036 - dense_1_loss_22: 0.3709 - dense_1_loss_23: 0.3572 - dense_1_loss_24: 0.3352 - dense_1_loss_25: 0.3667 - dense_1_loss_26: 0.3560 - dense_1_loss_27: 0.3616 - dense_1_loss_28: 0.4163 - dense_1_loss_29: 0.4613 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 44/100 60/60 [==============================] - 0s - loss: 17.6604 - dense_1_loss_1: 3.9539 - dense_1_loss_2: 2.4550 - dense_1_loss_3: 1.3561 - dense_1_loss_4: 0.8524 - dense_1_loss_5: 0.6392 - dense_1_loss_6: 0.4584 - dense_1_loss_7: 0.4742 - dense_1_loss_8: 0.3948 - dense_1_loss_9: 0.3857 - dense_1_loss_10: 0.3302 - dense_1_loss_11: 0.3105 - dense_1_loss_12: 0.2922 - dense_1_loss_13: 0.2691 - dense_1_loss_14: 0.3159 - dense_1_loss_15: 0.3384 - dense_1_loss_16: 0.3200 - dense_1_loss_17: 0.3353 - dense_1_loss_18: 0.3191 - dense_1_loss_19: 0.3258 - dense_1_loss_20: 0.3383 - dense_1_loss_21: 0.3766 - dense_1_loss_22: 0.3449 - dense_1_loss_23: 0.3322 - dense_1_loss_24: 0.3100 - dense_1_loss_25: 0.3369 - dense_1_loss_26: 0.3363 - dense_1_loss_27: 0.3341 - dense_1_loss_28: 0.3910 - dense_1_loss_29: 0.4339 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 45/100 60/60 [==============================] - 0s - loss: 16.8571 - dense_1_loss_1: 3.9492 - dense_1_loss_2: 2.4136 - dense_1_loss_3: 1.3123 - dense_1_loss_4: 0.8092 - dense_1_loss_5: 0.6031 - dense_1_loss_6: 0.4272 - dense_1_loss_7: 0.4441 - dense_1_loss_8: 0.3653 - dense_1_loss_9: 0.3573 - dense_1_loss_10: 0.3042 - dense_1_loss_11: 0.2883 - dense_1_loss_12: 0.2697 - dense_1_loss_13: 0.2469 - dense_1_loss_14: 0.2894 - dense_1_loss_15: 0.3125 - dense_1_loss_16: 0.2962 - dense_1_loss_17: 0.3092 - dense_1_loss_18: 0.2941 - dense_1_loss_19: 0.2973 - dense_1_loss_20: 0.3131 - dense_1_loss_21: 0.3520 - dense_1_loss_22: 0.3178 - dense_1_loss_23: 0.3077 - dense_1_loss_24: 0.2854 - dense_1_loss_25: 0.3166 - dense_1_loss_26: 0.3098 - dense_1_loss_27: 0.3033 - dense_1_loss_28: 0.3577 - dense_1_loss_29: 0.4043 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 46/100 60/60 [==============================] - 0s - loss: 16.1382 - dense_1_loss_1: 3.9435 - dense_1_loss_2: 2.3732 - dense_1_loss_3: 1.2737 - dense_1_loss_4: 0.7647 - dense_1_loss_5: 0.5691 - dense_1_loss_6: 0.4025 - dense_1_loss_7: 0.4178 - dense_1_loss_8: 0.3399 - dense_1_loss_9: 0.3340 - dense_1_loss_10: 0.2810 - dense_1_loss_11: 0.2693 - dense_1_loss_12: 0.2492 - dense_1_loss_13: 0.2290 - dense_1_loss_14: 0.2659 - dense_1_loss_15: 0.2900 - dense_1_loss_16: 0.2751 - dense_1_loss_17: 0.2862 - dense_1_loss_18: 0.2709 - dense_1_loss_19: 0.2747 - dense_1_loss_20: 0.2914 - dense_1_loss_21: 0.3273 - dense_1_loss_22: 0.2908 - dense_1_loss_23: 0.2859 - dense_1_loss_24: 0.2608 - dense_1_loss_25: 0.2985 - dense_1_loss_26: 0.2862 - dense_1_loss_27: 0.2815 - dense_1_loss_28: 0.3282 - dense_1_loss_29: 0.3778 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8000 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 47/100 60/60 [==============================] - 0s - loss: 15.4720 - dense_1_loss_1: 3.9387 - dense_1_loss_2: 2.3342 - dense_1_loss_3: 1.2363 - dense_1_loss_4: 0.7245 - dense_1_loss_5: 0.5361 - dense_1_loss_6: 0.3785 - dense_1_loss_7: 0.3913 - dense_1_loss_8: 0.3174 - dense_1_loss_9: 0.3112 - dense_1_loss_10: 0.2612 - dense_1_loss_11: 0.2487 - dense_1_loss_12: 0.2289 - dense_1_loss_13: 0.2127 - dense_1_loss_14: 0.2464 - dense_1_loss_15: 0.2676 - dense_1_loss_16: 0.2546 - dense_1_loss_17: 0.2654 - dense_1_loss_18: 0.2536 - dense_1_loss_19: 0.2544 - dense_1_loss_20: 0.2686 - dense_1_loss_21: 0.3038 - dense_1_loss_22: 0.2698 - dense_1_loss_23: 0.2642 - dense_1_loss_24: 0.2424 - dense_1_loss_25: 0.2721 - dense_1_loss_26: 0.2640 - dense_1_loss_27: 0.2639 - dense_1_loss_28: 0.3053 - dense_1_loss_29: 0.3563 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9167 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 48/100 60/60 [==============================] - 0s - loss: 14.8465 - dense_1_loss_1: 3.9343 - dense_1_loss_2: 2.2962 - dense_1_loss_3: 1.2012 - dense_1_loss_4: 0.6848 - dense_1_loss_5: 0.5051 - dense_1_loss_6: 0.3525 - dense_1_loss_7: 0.3689 - dense_1_loss_8: 0.2953 - dense_1_loss_9: 0.2888 - dense_1_loss_10: 0.2447 - dense_1_loss_11: 0.2305 - dense_1_loss_12: 0.2116 - dense_1_loss_13: 0.1979 - dense_1_loss_14: 0.2309 - dense_1_loss_15: 0.2471 - dense_1_loss_16: 0.2345 - dense_1_loss_17: 0.2451 - dense_1_loss_18: 0.2351 - dense_1_loss_19: 0.2371 - dense_1_loss_20: 0.2489 - dense_1_loss_21: 0.2798 - dense_1_loss_22: 0.2504 - dense_1_loss_23: 0.2446 - dense_1_loss_24: 0.2260 - dense_1_loss_25: 0.2522 - dense_1_loss_26: 0.2430 - dense_1_loss_27: 0.2420 - dense_1_loss_28: 0.2851 - dense_1_loss_29: 0.3326 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9333 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 49/100 60/60 [==============================] - 0s - loss: 14.2739 - dense_1_loss_1: 3.9297 - dense_1_loss_2: 2.2604 - dense_1_loss_3: 1.1640 - dense_1_loss_4: 0.6513 - dense_1_loss_5: 0.4757 - dense_1_loss_6: 0.3300 - dense_1_loss_7: 0.3501 - dense_1_loss_8: 0.2753 - dense_1_loss_9: 0.2698 - dense_1_loss_10: 0.2278 - dense_1_loss_11: 0.2160 - dense_1_loss_12: 0.1971 - dense_1_loss_13: 0.1839 - dense_1_loss_14: 0.2150 - dense_1_loss_15: 0.2288 - dense_1_loss_16: 0.2164 - dense_1_loss_17: 0.2285 - dense_1_loss_18: 0.2171 - dense_1_loss_19: 0.2213 - dense_1_loss_20: 0.2315 - dense_1_loss_21: 0.2571 - dense_1_loss_22: 0.2315 - dense_1_loss_23: 0.2280 - dense_1_loss_24: 0.2112 - dense_1_loss_25: 0.2325 - dense_1_loss_26: 0.2239 - dense_1_loss_27: 0.2240 - dense_1_loss_28: 0.2655 - dense_1_loss_29: 0.3106 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 50/100 60/60 [==============================] - 0s - loss: 13.7446 - dense_1_loss_1: 3.9250 - dense_1_loss_2: 2.2241 - dense_1_loss_3: 1.1303 - dense_1_loss_4: 0.6156 - dense_1_loss_5: 0.4484 - dense_1_loss_6: 0.3100 - dense_1_loss_7: 0.3306 - dense_1_loss_8: 0.2544 - dense_1_loss_9: 0.2496 - dense_1_loss_10: 0.2109 - dense_1_loss_11: 0.2003 - dense_1_loss_12: 0.1842 - dense_1_loss_13: 0.1699 - dense_1_loss_14: 0.1996 - dense_1_loss_15: 0.2118 - dense_1_loss_16: 0.2002 - dense_1_loss_17: 0.2130 - dense_1_loss_18: 0.2017 - dense_1_loss_19: 0.2052 - dense_1_loss_20: 0.2148 - dense_1_loss_21: 0.2425 - dense_1_loss_22: 0.2154 - dense_1_loss_23: 0.2112 - dense_1_loss_24: 0.1980 - dense_1_loss_25: 0.2131 - dense_1_loss_26: 0.2105 - dense_1_loss_27: 0.2103 - dense_1_loss_28: 0.2498 - dense_1_loss_29: 0.2939 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 51/100 60/60 [==============================] - 0s - loss: 13.2603 - dense_1_loss_1: 3.9204 - dense_1_loss_2: 2.1893 - dense_1_loss_3: 1.0985 - dense_1_loss_4: 0.5849 - dense_1_loss_5: 0.4239 - dense_1_loss_6: 0.2920 - dense_1_loss_7: 0.3140 - dense_1_loss_8: 0.2371 - dense_1_loss_9: 0.2322 - dense_1_loss_10: 0.1956 - dense_1_loss_11: 0.1865 - dense_1_loss_12: 0.1720 - dense_1_loss_13: 0.1583 - dense_1_loss_14: 0.1849 - dense_1_loss_15: 0.1978 - dense_1_loss_16: 0.1860 - dense_1_loss_17: 0.1981 - dense_1_loss_18: 0.1883 - dense_1_loss_19: 0.1895 - dense_1_loss_20: 0.1995 - dense_1_loss_21: 0.2265 - dense_1_loss_22: 0.2011 - dense_1_loss_23: 0.1966 - dense_1_loss_24: 0.1860 - dense_1_loss_25: 0.1967 - dense_1_loss_26: 0.1974 - dense_1_loss_27: 0.1944 - dense_1_loss_28: 0.2351 - dense_1_loss_29: 0.2779 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.7167 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0167 Epoch 52/100 60/60 [==============================] - 0s - loss: 12.8042 - dense_1_loss_1: 3.9163 - dense_1_loss_2: 2.1564 - dense_1_loss_3: 1.0675 - dense_1_loss_4: 0.5564 - dense_1_loss_5: 0.4035 - dense_1_loss_6: 0.2752 - dense_1_loss_7: 0.2994 - dense_1_loss_8: 0.2228 - dense_1_loss_9: 0.2164 - dense_1_loss_10: 0.1839 - dense_1_loss_11: 0.1746 - dense_1_loss_12: 0.1598 - dense_1_loss_13: 0.1475 - dense_1_loss_14: 0.1715 - dense_1_loss_15: 0.1857 - dense_1_loss_16: 0.1743 - dense_1_loss_17: 0.1853 - dense_1_loss_18: 0.1761 - dense_1_loss_19: 0.1763 - dense_1_loss_20: 0.1857 - dense_1_loss_21: 0.2083 - dense_1_loss_22: 0.1857 - dense_1_loss_23: 0.1818 - dense_1_loss_24: 0.1720 - dense_1_loss_25: 0.1864 - dense_1_loss_26: 0.1827 - dense_1_loss_27: 0.1774 - dense_1_loss_28: 0.2179 - dense_1_loss_29: 0.2575 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 53/100 60/60 [==============================] - 0s - loss: 12.3999 - dense_1_loss_1: 3.9116 - dense_1_loss_2: 2.1240 - dense_1_loss_3: 1.0388 - dense_1_loss_4: 0.5297 - dense_1_loss_5: 0.3823 - dense_1_loss_6: 0.2611 - dense_1_loss_7: 0.2856 - dense_1_loss_8: 0.2096 - dense_1_loss_9: 0.2032 - dense_1_loss_10: 0.1722 - dense_1_loss_11: 0.1640 - dense_1_loss_12: 0.1491 - dense_1_loss_13: 0.1387 - dense_1_loss_14: 0.1608 - dense_1_loss_15: 0.1739 - dense_1_loss_16: 0.1635 - dense_1_loss_17: 0.1725 - dense_1_loss_18: 0.1650 - dense_1_loss_19: 0.1655 - dense_1_loss_20: 0.1738 - dense_1_loss_21: 0.1928 - dense_1_loss_22: 0.1735 - dense_1_loss_23: 0.1696 - dense_1_loss_24: 0.1612 - dense_1_loss_25: 0.1742 - dense_1_loss_26: 0.1701 - dense_1_loss_27: 0.1651 - dense_1_loss_28: 0.2058 - dense_1_loss_29: 0.2425 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 54/100 60/60 [==============================] - 0s - loss: 12.0134 - dense_1_loss_1: 3.9074 - dense_1_loss_2: 2.0932 - dense_1_loss_3: 1.0108 - dense_1_loss_4: 0.5044 - dense_1_loss_5: 0.3612 - dense_1_loss_6: 0.2464 - dense_1_loss_7: 0.2697 - dense_1_loss_8: 0.1961 - dense_1_loss_9: 0.1897 - dense_1_loss_10: 0.1609 - dense_1_loss_11: 0.1551 - dense_1_loss_12: 0.1399 - dense_1_loss_13: 0.1299 - dense_1_loss_14: 0.1507 - dense_1_loss_15: 0.1617 - dense_1_loss_16: 0.1530 - dense_1_loss_17: 0.1611 - dense_1_loss_18: 0.1551 - dense_1_loss_19: 0.1552 - dense_1_loss_20: 0.1622 - dense_1_loss_21: 0.1797 - dense_1_loss_22: 0.1626 - dense_1_loss_23: 0.1590 - dense_1_loss_24: 0.1508 - dense_1_loss_25: 0.1623 - dense_1_loss_26: 0.1585 - dense_1_loss_27: 0.1562 - dense_1_loss_28: 0.1939 - dense_1_loss_29: 0.2269 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9167 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 55/100 60/60 [==============================] - 0s - loss: 11.6616 - dense_1_loss_1: 3.9031 - dense_1_loss_2: 2.0611 - dense_1_loss_3: 0.9840 - dense_1_loss_4: 0.4819 - dense_1_loss_5: 0.3436 - dense_1_loss_6: 0.2327 - dense_1_loss_7: 0.2579 - dense_1_loss_8: 0.1852 - dense_1_loss_9: 0.1780 - dense_1_loss_10: 0.1497 - dense_1_loss_11: 0.1462 - dense_1_loss_12: 0.1321 - dense_1_loss_13: 0.1220 - dense_1_loss_14: 0.1420 - dense_1_loss_15: 0.1513 - dense_1_loss_16: 0.1437 - dense_1_loss_17: 0.1525 - dense_1_loss_18: 0.1476 - dense_1_loss_19: 0.1459 - dense_1_loss_20: 0.1523 - dense_1_loss_21: 0.1682 - dense_1_loss_22: 0.1530 - dense_1_loss_23: 0.1518 - dense_1_loss_24: 0.1409 - dense_1_loss_25: 0.1548 - dense_1_loss_26: 0.1502 - dense_1_loss_27: 0.1464 - dense_1_loss_28: 0.1748 - dense_1_loss_29: 0.2085 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 56/100 60/60 [==============================] - 0s - loss: 11.3225 - dense_1_loss_1: 3.8993 - dense_1_loss_2: 2.0332 - dense_1_loss_3: 0.9570 - dense_1_loss_4: 0.4600 - dense_1_loss_5: 0.3255 - dense_1_loss_6: 0.2192 - dense_1_loss_7: 0.2450 - dense_1_loss_8: 0.1735 - dense_1_loss_9: 0.1664 - dense_1_loss_10: 0.1409 - dense_1_loss_11: 0.1362 - dense_1_loss_12: 0.1245 - dense_1_loss_13: 0.1147 - dense_1_loss_14: 0.1330 - dense_1_loss_15: 0.1411 - dense_1_loss_16: 0.1344 - dense_1_loss_17: 0.1432 - dense_1_loss_18: 0.1391 - dense_1_loss_19: 0.1371 - dense_1_loss_20: 0.1413 - dense_1_loss_21: 0.1572 - dense_1_loss_22: 0.1436 - dense_1_loss_23: 0.1430 - dense_1_loss_24: 0.1325 - dense_1_loss_25: 0.1432 - dense_1_loss_26: 0.1394 - dense_1_loss_27: 0.1382 - dense_1_loss_28: 0.1650 - dense_1_loss_29: 0.1959 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 57/100 60/60 [==============================] - 0s - loss: 11.0225 - dense_1_loss_1: 3.8953 - dense_1_loss_2: 2.0038 - dense_1_loss_3: 0.9324 - dense_1_loss_4: 0.4391 - dense_1_loss_5: 0.3103 - dense_1_loss_6: 0.2078 - dense_1_loss_7: 0.2353 - dense_1_loss_8: 0.1638 - dense_1_loss_9: 0.1569 - dense_1_loss_10: 0.1339 - dense_1_loss_11: 0.1282 - dense_1_loss_12: 0.1178 - dense_1_loss_13: 0.1083 - dense_1_loss_14: 0.1254 - dense_1_loss_15: 0.1323 - dense_1_loss_16: 0.1263 - dense_1_loss_17: 0.1347 - dense_1_loss_18: 0.1299 - dense_1_loss_19: 0.1296 - dense_1_loss_20: 0.1325 - dense_1_loss_21: 0.1465 - dense_1_loss_22: 0.1344 - dense_1_loss_23: 0.1342 - dense_1_loss_24: 0.1257 - dense_1_loss_25: 0.1335 - dense_1_loss_26: 0.1298 - dense_1_loss_27: 0.1305 - dense_1_loss_28: 0.1583 - dense_1_loss_29: 0.1860 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 58/100 60/60 [==============================] - 0s - loss: 10.7380 - dense_1_loss_1: 3.8911 - dense_1_loss_2: 1.9758 - dense_1_loss_3: 0.9108 - dense_1_loss_4: 0.4205 - dense_1_loss_5: 0.2954 - dense_1_loss_6: 0.1975 - dense_1_loss_7: 0.2254 - dense_1_loss_8: 0.1546 - dense_1_loss_9: 0.1467 - dense_1_loss_10: 0.1262 - dense_1_loss_11: 0.1204 - dense_1_loss_12: 0.1107 - dense_1_loss_13: 0.1026 - dense_1_loss_14: 0.1174 - dense_1_loss_15: 0.1248 - dense_1_loss_16: 0.1190 - dense_1_loss_17: 0.1275 - dense_1_loss_18: 0.1219 - dense_1_loss_19: 0.1216 - dense_1_loss_20: 0.1245 - dense_1_loss_21: 0.1374 - dense_1_loss_22: 0.1258 - dense_1_loss_23: 0.1266 - dense_1_loss_24: 0.1186 - dense_1_loss_25: 0.1274 - dense_1_loss_26: 0.1231 - dense_1_loss_27: 0.1232 - dense_1_loss_28: 0.1463 - dense_1_loss_29: 0.1755 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 59/100 60/60 [==============================] - 0s - loss: 10.4689 - dense_1_loss_1: 3.8876 - dense_1_loss_2: 1.9496 - dense_1_loss_3: 0.8867 - dense_1_loss_4: 0.4027 - dense_1_loss_5: 0.2801 - dense_1_loss_6: 0.1887 - dense_1_loss_7: 0.2153 - dense_1_loss_8: 0.1457 - dense_1_loss_9: 0.1376 - dense_1_loss_10: 0.1183 - dense_1_loss_11: 0.1136 - dense_1_loss_12: 0.1049 - dense_1_loss_13: 0.0971 - dense_1_loss_14: 0.1096 - dense_1_loss_15: 0.1183 - dense_1_loss_16: 0.1126 - dense_1_loss_17: 0.1191 - dense_1_loss_18: 0.1150 - dense_1_loss_19: 0.1143 - dense_1_loss_20: 0.1172 - dense_1_loss_21: 0.1295 - dense_1_loss_22: 0.1180 - dense_1_loss_23: 0.1178 - dense_1_loss_24: 0.1127 - dense_1_loss_25: 0.1191 - dense_1_loss_26: 0.1145 - dense_1_loss_27: 0.1176 - dense_1_loss_28: 0.1402 - dense_1_loss_29: 0.1655 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 60/100 60/60 [==============================] - 0s - loss: 10.2251 - dense_1_loss_1: 3.8837 - dense_1_loss_2: 1.9230 - dense_1_loss_3: 0.8655 - dense_1_loss_4: 0.3860 - dense_1_loss_5: 0.2658 - dense_1_loss_6: 0.1796 - dense_1_loss_7: 0.2049 - dense_1_loss_8: 0.1378 - dense_1_loss_9: 0.1289 - dense_1_loss_10: 0.1118 - dense_1_loss_11: 0.1079 - dense_1_loss_12: 0.0999 - dense_1_loss_13: 0.0919 - dense_1_loss_14: 0.1036 - dense_1_loss_15: 0.1125 - dense_1_loss_16: 0.1067 - dense_1_loss_17: 0.1129 - dense_1_loss_18: 0.1092 - dense_1_loss_19: 0.1080 - dense_1_loss_20: 0.1114 - dense_1_loss_21: 0.1226 - dense_1_loss_22: 0.1113 - dense_1_loss_23: 0.1116 - dense_1_loss_24: 0.1069 - dense_1_loss_25: 0.1140 - dense_1_loss_26: 0.1089 - dense_1_loss_27: 0.1114 - dense_1_loss_28: 0.1307 - dense_1_loss_29: 0.1569 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 61/100 60/60 [==============================] - 0s - loss: 9.9980 - dense_1_loss_1: 3.8798 - dense_1_loss_2: 1.8981 - dense_1_loss_3: 0.8457 - dense_1_loss_4: 0.3699 - dense_1_loss_5: 0.2550 - dense_1_loss_6: 0.1711 - dense_1_loss_7: 0.1973 - dense_1_loss_8: 0.1313 - dense_1_loss_9: 0.1223 - dense_1_loss_10: 0.1058 - dense_1_loss_11: 0.1023 - dense_1_loss_12: 0.0953 - dense_1_loss_13: 0.0867 - dense_1_loss_14: 0.0987 - dense_1_loss_15: 0.1064 - dense_1_loss_16: 0.1010 - dense_1_loss_17: 0.1067 - dense_1_loss_18: 0.1033 - dense_1_loss_19: 0.1020 - dense_1_loss_20: 0.1053 - dense_1_loss_21: 0.1154 - dense_1_loss_22: 0.1048 - dense_1_loss_23: 0.1062 - dense_1_loss_24: 0.1008 - dense_1_loss_25: 0.1099 - dense_1_loss_26: 0.1037 - dense_1_loss_27: 0.1049 - dense_1_loss_28: 0.1213 - dense_1_loss_29: 0.1471 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4667 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 62/100 60/60 [==============================] - 0s - loss: 9.7906 - dense_1_loss_1: 3.8762 - dense_1_loss_2: 1.8735 - dense_1_loss_3: 0.8256 - dense_1_loss_4: 0.3560 - dense_1_loss_5: 0.2434 - dense_1_loss_6: 0.1634 - dense_1_loss_7: 0.1891 - dense_1_loss_8: 0.1245 - dense_1_loss_9: 0.1159 - dense_1_loss_10: 0.1016 - dense_1_loss_11: 0.0973 - dense_1_loss_12: 0.0910 - dense_1_loss_13: 0.0828 - dense_1_loss_14: 0.0942 - dense_1_loss_15: 0.1009 - dense_1_loss_16: 0.0956 - dense_1_loss_17: 0.1009 - dense_1_loss_18: 0.0981 - dense_1_loss_19: 0.0978 - dense_1_loss_20: 0.0998 - dense_1_loss_21: 0.1090 - dense_1_loss_22: 0.0994 - dense_1_loss_23: 0.0995 - dense_1_loss_24: 0.0958 - dense_1_loss_25: 0.1017 - dense_1_loss_26: 0.0961 - dense_1_loss_27: 0.1000 - dense_1_loss_28: 0.1213 - dense_1_loss_29: 0.1400 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 63/100 60/60 [==============================] - 0s - loss: 9.5919 - dense_1_loss_1: 3.8729 - dense_1_loss_2: 1.8494 - dense_1_loss_3: 0.8063 - dense_1_loss_4: 0.3417 - dense_1_loss_5: 0.2325 - dense_1_loss_6: 0.1558 - dense_1_loss_7: 0.1811 - dense_1_loss_8: 0.1182 - dense_1_loss_9: 0.1100 - dense_1_loss_10: 0.0965 - dense_1_loss_11: 0.0928 - dense_1_loss_12: 0.0864 - dense_1_loss_13: 0.0786 - dense_1_loss_14: 0.0901 - dense_1_loss_15: 0.0951 - dense_1_loss_16: 0.0912 - dense_1_loss_17: 0.0951 - dense_1_loss_18: 0.0931 - dense_1_loss_19: 0.0930 - dense_1_loss_20: 0.0940 - dense_1_loss_21: 0.1036 - dense_1_loss_22: 0.0942 - dense_1_loss_23: 0.0939 - dense_1_loss_24: 0.0915 - dense_1_loss_25: 0.0960 - dense_1_loss_26: 0.0912 - dense_1_loss_27: 0.0946 - dense_1_loss_28: 0.1196 - dense_1_loss_29: 0.1336 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 64/100 60/60 [==============================] - 0s - loss: 9.3998 - dense_1_loss_1: 3.8692 - dense_1_loss_2: 1.8273 - dense_1_loss_3: 0.7880 - dense_1_loss_4: 0.3291 - dense_1_loss_5: 0.2220 - dense_1_loss_6: 0.1479 - dense_1_loss_7: 0.1728 - dense_1_loss_8: 0.1132 - dense_1_loss_9: 0.1037 - dense_1_loss_10: 0.0915 - dense_1_loss_11: 0.0891 - dense_1_loss_12: 0.0816 - dense_1_loss_13: 0.0749 - dense_1_loss_14: 0.0861 - dense_1_loss_15: 0.0901 - dense_1_loss_16: 0.0869 - dense_1_loss_17: 0.0910 - dense_1_loss_18: 0.0887 - dense_1_loss_19: 0.0874 - dense_1_loss_20: 0.0890 - dense_1_loss_21: 0.0987 - dense_1_loss_22: 0.0899 - dense_1_loss_23: 0.0905 - dense_1_loss_24: 0.0871 - dense_1_loss_25: 0.0939 - dense_1_loss_26: 0.0890 - dense_1_loss_27: 0.0895 - dense_1_loss_28: 0.1062 - dense_1_loss_29: 0.1256 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 65/100 60/60 [==============================] - 0s - loss: 9.2249 - dense_1_loss_1: 3.8660 - dense_1_loss_2: 1.8046 - dense_1_loss_3: 0.7703 - dense_1_loss_4: 0.3173 - dense_1_loss_5: 0.2125 - dense_1_loss_6: 0.1422 - dense_1_loss_7: 0.1658 - dense_1_loss_8: 0.1075 - dense_1_loss_9: 0.0988 - dense_1_loss_10: 0.0872 - dense_1_loss_11: 0.0853 - dense_1_loss_12: 0.0781 - dense_1_loss_13: 0.0713 - dense_1_loss_14: 0.0821 - dense_1_loss_15: 0.0856 - dense_1_loss_16: 0.0829 - dense_1_loss_17: 0.0865 - dense_1_loss_18: 0.0846 - dense_1_loss_19: 0.0839 - dense_1_loss_20: 0.0844 - dense_1_loss_21: 0.0935 - dense_1_loss_22: 0.0861 - dense_1_loss_23: 0.0854 - dense_1_loss_24: 0.0833 - dense_1_loss_25: 0.0889 - dense_1_loss_26: 0.0838 - dense_1_loss_27: 0.0858 - dense_1_loss_28: 0.1023 - dense_1_loss_29: 0.1188 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8000 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0167 Epoch 66/100 60/60 [==============================] - 0s - loss: 9.0638 - dense_1_loss_1: 3.8627 - dense_1_loss_2: 1.7830 - dense_1_loss_3: 0.7524 - dense_1_loss_4: 0.3050 - dense_1_loss_5: 0.2023 - dense_1_loss_6: 0.1376 - dense_1_loss_7: 0.1590 - dense_1_loss_8: 0.1030 - dense_1_loss_9: 0.0939 - dense_1_loss_10: 0.0836 - dense_1_loss_11: 0.0807 - dense_1_loss_12: 0.0749 - dense_1_loss_13: 0.0683 - dense_1_loss_14: 0.0788 - dense_1_loss_15: 0.0812 - dense_1_loss_16: 0.0794 - dense_1_loss_17: 0.0821 - dense_1_loss_18: 0.0803 - dense_1_loss_19: 0.0812 - dense_1_loss_20: 0.0804 - dense_1_loss_21: 0.0884 - dense_1_loss_22: 0.0819 - dense_1_loss_23: 0.0804 - dense_1_loss_24: 0.0797 - dense_1_loss_25: 0.0835 - dense_1_loss_26: 0.0785 - dense_1_loss_27: 0.0831 - dense_1_loss_28: 0.1049 - dense_1_loss_29: 0.1135 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 67/100 60/60 [==============================] - 0s - loss: 8.9063 - dense_1_loss_1: 3.8593 - dense_1_loss_2: 1.7616 - dense_1_loss_3: 0.7363 - dense_1_loss_4: 0.2945 - dense_1_loss_5: 0.1951 - dense_1_loss_6: 0.1315 - dense_1_loss_7: 0.1540 - dense_1_loss_8: 0.0990 - dense_1_loss_9: 0.0899 - dense_1_loss_10: 0.0804 - dense_1_loss_11: 0.0772 - dense_1_loss_12: 0.0720 - dense_1_loss_13: 0.0656 - dense_1_loss_14: 0.0754 - dense_1_loss_15: 0.0779 - dense_1_loss_16: 0.0757 - dense_1_loss_17: 0.0785 - dense_1_loss_18: 0.0770 - dense_1_loss_19: 0.0770 - dense_1_loss_20: 0.0767 - dense_1_loss_21: 0.0846 - dense_1_loss_22: 0.0780 - dense_1_loss_23: 0.0773 - dense_1_loss_24: 0.0757 - dense_1_loss_25: 0.0811 - dense_1_loss_26: 0.0756 - dense_1_loss_27: 0.0788 - dense_1_loss_28: 0.0944 - dense_1_loss_29: 0.1061 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 68/100 60/60 [==============================] - 0s - loss: 8.7648 - dense_1_loss_1: 3.8561 - dense_1_loss_2: 1.7406 - dense_1_loss_3: 0.7209 - dense_1_loss_4: 0.2843 - dense_1_loss_5: 0.1868 - dense_1_loss_6: 0.1258 - dense_1_loss_7: 0.1477 - dense_1_loss_8: 0.0949 - dense_1_loss_9: 0.0859 - dense_1_loss_10: 0.0769 - dense_1_loss_11: 0.0742 - dense_1_loss_12: 0.0692 - dense_1_loss_13: 0.0631 - dense_1_loss_14: 0.0723 - dense_1_loss_15: 0.0749 - dense_1_loss_16: 0.0724 - dense_1_loss_17: 0.0756 - dense_1_loss_18: 0.0741 - dense_1_loss_19: 0.0733 - dense_1_loss_20: 0.0737 - dense_1_loss_21: 0.0812 - dense_1_loss_22: 0.0744 - dense_1_loss_23: 0.0748 - dense_1_loss_24: 0.0724 - dense_1_loss_25: 0.0792 - dense_1_loss_26: 0.0738 - dense_1_loss_27: 0.0755 - dense_1_loss_28: 0.0884 - dense_1_loss_29: 0.1022 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 69/100 60/60 [==============================] - 0s - loss: 8.6258 - dense_1_loss_1: 3.8530 - dense_1_loss_2: 1.7212 - dense_1_loss_3: 0.7066 - dense_1_loss_4: 0.2736 - dense_1_loss_5: 0.1787 - dense_1_loss_6: 0.1213 - dense_1_loss_7: 0.1411 - dense_1_loss_8: 0.0909 - dense_1_loss_9: 0.0819 - dense_1_loss_10: 0.0738 - dense_1_loss_11: 0.0708 - dense_1_loss_12: 0.0662 - dense_1_loss_13: 0.0606 - dense_1_loss_14: 0.0689 - dense_1_loss_15: 0.0720 - dense_1_loss_16: 0.0694 - dense_1_loss_17: 0.0719 - dense_1_loss_18: 0.0711 - dense_1_loss_19: 0.0703 - dense_1_loss_20: 0.0701 - dense_1_loss_21: 0.0777 - dense_1_loss_22: 0.0710 - dense_1_loss_23: 0.0705 - dense_1_loss_24: 0.0698 - dense_1_loss_25: 0.0731 - dense_1_loss_26: 0.0688 - dense_1_loss_27: 0.0730 - dense_1_loss_28: 0.0909 - dense_1_loss_29: 0.0975 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 70/100 60/60 [==============================] - 0s - loss: 8.4981 - dense_1_loss_1: 3.8499 - dense_1_loss_2: 1.7014 - dense_1_loss_3: 0.6921 - dense_1_loss_4: 0.2641 - dense_1_loss_5: 0.1717 - dense_1_loss_6: 0.1169 - dense_1_loss_7: 0.1354 - dense_1_loss_8: 0.0876 - dense_1_loss_9: 0.0785 - dense_1_loss_10: 0.0708 - dense_1_loss_11: 0.0681 - dense_1_loss_12: 0.0636 - dense_1_loss_13: 0.0583 - dense_1_loss_14: 0.0663 - dense_1_loss_15: 0.0690 - dense_1_loss_16: 0.0667 - dense_1_loss_17: 0.0691 - dense_1_loss_18: 0.0682 - dense_1_loss_19: 0.0678 - dense_1_loss_20: 0.0672 - dense_1_loss_21: 0.0742 - dense_1_loss_22: 0.0684 - dense_1_loss_23: 0.0678 - dense_1_loss_24: 0.0669 - dense_1_loss_25: 0.0703 - dense_1_loss_26: 0.0659 - dense_1_loss_27: 0.0709 - dense_1_loss_28: 0.0873 - dense_1_loss_29: 0.0937 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 71/100 60/60 [==============================] - 0s - loss: 8.3733 - dense_1_loss_1: 3.8468 - dense_1_loss_2: 1.6830 - dense_1_loss_3: 0.6775 - dense_1_loss_4: 0.2550 - dense_1_loss_5: 0.1650 - dense_1_loss_6: 0.1127 - dense_1_loss_7: 0.1301 - dense_1_loss_8: 0.0846 - dense_1_loss_9: 0.0752 - dense_1_loss_10: 0.0680 - dense_1_loss_11: 0.0660 - dense_1_loss_12: 0.0615 - dense_1_loss_13: 0.0562 - dense_1_loss_14: 0.0637 - dense_1_loss_15: 0.0666 - dense_1_loss_16: 0.0640 - dense_1_loss_17: 0.0667 - dense_1_loss_18: 0.0655 - dense_1_loss_19: 0.0648 - dense_1_loss_20: 0.0648 - dense_1_loss_21: 0.0711 - dense_1_loss_22: 0.0655 - dense_1_loss_23: 0.0659 - dense_1_loss_24: 0.0638 - dense_1_loss_25: 0.0693 - dense_1_loss_26: 0.0641 - dense_1_loss_27: 0.0683 - dense_1_loss_28: 0.0786 - dense_1_loss_29: 0.0889 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 72/100 60/60 [==============================] - 0s - loss: 8.2575 - dense_1_loss_1: 3.8439 - dense_1_loss_2: 1.6643 - dense_1_loss_3: 0.6636 - dense_1_loss_4: 0.2477 - dense_1_loss_5: 0.1588 - dense_1_loss_6: 0.1090 - dense_1_loss_7: 0.1248 - dense_1_loss_8: 0.0817 - dense_1_loss_9: 0.0722 - dense_1_loss_10: 0.0653 - dense_1_loss_11: 0.0635 - dense_1_loss_12: 0.0594 - dense_1_loss_13: 0.0542 - dense_1_loss_14: 0.0612 - dense_1_loss_15: 0.0642 - dense_1_loss_16: 0.0618 - dense_1_loss_17: 0.0639 - dense_1_loss_18: 0.0629 - dense_1_loss_19: 0.0626 - dense_1_loss_20: 0.0624 - dense_1_loss_21: 0.0682 - dense_1_loss_22: 0.0629 - dense_1_loss_23: 0.0634 - dense_1_loss_24: 0.0615 - dense_1_loss_25: 0.0670 - dense_1_loss_26: 0.0612 - dense_1_loss_27: 0.0661 - dense_1_loss_28: 0.0755 - dense_1_loss_29: 0.0841 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 73/100 60/60 [==============================] - 0s - loss: 8.1515 - dense_1_loss_1: 3.8407 - dense_1_loss_2: 1.6471 - dense_1_loss_3: 0.6508 - dense_1_loss_4: 0.2398 - dense_1_loss_5: 0.1534 - dense_1_loss_6: 0.1058 - dense_1_loss_7: 0.1208 - dense_1_loss_8: 0.0788 - dense_1_loss_9: 0.0695 - dense_1_loss_10: 0.0631 - dense_1_loss_11: 0.0609 - dense_1_loss_12: 0.0577 - dense_1_loss_13: 0.0523 - dense_1_loss_14: 0.0590 - dense_1_loss_15: 0.0618 - dense_1_loss_16: 0.0597 - dense_1_loss_17: 0.0614 - dense_1_loss_18: 0.0606 - dense_1_loss_19: 0.0610 - dense_1_loss_20: 0.0599 - dense_1_loss_21: 0.0653 - dense_1_loss_22: 0.0604 - dense_1_loss_23: 0.0606 - dense_1_loss_24: 0.0594 - dense_1_loss_25: 0.0637 - dense_1_loss_26: 0.0579 - dense_1_loss_27: 0.0640 - dense_1_loss_28: 0.0760 - dense_1_loss_29: 0.0802 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 74/100 60/60 [==============================] - 0s - loss: 8.0426 - dense_1_loss_1: 3.8377 - dense_1_loss_2: 1.6299 - dense_1_loss_3: 0.6380 - dense_1_loss_4: 0.2312 - dense_1_loss_5: 0.1471 - dense_1_loss_6: 0.1023 - dense_1_loss_7: 0.1148 - dense_1_loss_8: 0.0758 - dense_1_loss_9: 0.0668 - dense_1_loss_10: 0.0607 - dense_1_loss_11: 0.0590 - dense_1_loss_12: 0.0556 - dense_1_loss_13: 0.0504 - dense_1_loss_14: 0.0571 - dense_1_loss_15: 0.0594 - dense_1_loss_16: 0.0575 - dense_1_loss_17: 0.0591 - dense_1_loss_18: 0.0586 - dense_1_loss_19: 0.0587 - dense_1_loss_20: 0.0574 - dense_1_loss_21: 0.0631 - dense_1_loss_22: 0.0583 - dense_1_loss_23: 0.0584 - dense_1_loss_24: 0.0572 - dense_1_loss_25: 0.0614 - dense_1_loss_26: 0.0560 - dense_1_loss_27: 0.0618 - dense_1_loss_28: 0.0723 - dense_1_loss_29: 0.0769 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 75/100 60/60 [==============================] - 0s - loss: 7.9471 - dense_1_loss_1: 3.8350 - dense_1_loss_2: 1.6132 - dense_1_loss_3: 0.6262 - dense_1_loss_4: 0.2245 - dense_1_loss_5: 0.1421 - dense_1_loss_6: 0.0991 - dense_1_loss_7: 0.1104 - dense_1_loss_8: 0.0733 - dense_1_loss_9: 0.0645 - dense_1_loss_10: 0.0588 - dense_1_loss_11: 0.0571 - dense_1_loss_12: 0.0534 - dense_1_loss_13: 0.0489 - dense_1_loss_14: 0.0554 - dense_1_loss_15: 0.0570 - dense_1_loss_16: 0.0555 - dense_1_loss_17: 0.0571 - dense_1_loss_18: 0.0568 - dense_1_loss_19: 0.0565 - dense_1_loss_20: 0.0553 - dense_1_loss_21: 0.0610 - dense_1_loss_22: 0.0565 - dense_1_loss_23: 0.0566 - dense_1_loss_24: 0.0555 - dense_1_loss_25: 0.0596 - dense_1_loss_26: 0.0546 - dense_1_loss_27: 0.0598 - dense_1_loss_28: 0.0689 - dense_1_loss_29: 0.0747 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 76/100 60/60 [==============================] - 0s - loss: 7.8525 - dense_1_loss_1: 3.8321 - dense_1_loss_2: 1.5971 - dense_1_loss_3: 0.6137 - dense_1_loss_4: 0.2175 - dense_1_loss_5: 0.1371 - dense_1_loss_6: 0.0958 - dense_1_loss_7: 0.1061 - dense_1_loss_8: 0.0711 - dense_1_loss_9: 0.0623 - dense_1_loss_10: 0.0568 - dense_1_loss_11: 0.0554 - dense_1_loss_12: 0.0518 - dense_1_loss_13: 0.0473 - dense_1_loss_14: 0.0536 - dense_1_loss_15: 0.0552 - dense_1_loss_16: 0.0537 - dense_1_loss_17: 0.0552 - dense_1_loss_18: 0.0549 - dense_1_loss_19: 0.0543 - dense_1_loss_20: 0.0533 - dense_1_loss_21: 0.0590 - dense_1_loss_22: 0.0545 - dense_1_loss_23: 0.0550 - dense_1_loss_24: 0.0535 - dense_1_loss_25: 0.0581 - dense_1_loss_26: 0.0532 - dense_1_loss_27: 0.0580 - dense_1_loss_28: 0.0652 - dense_1_loss_29: 0.0718 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 77/100 60/60 [==============================] - 0s - loss: 7.7653 - dense_1_loss_1: 3.8292 - dense_1_loss_2: 1.5813 - dense_1_loss_3: 0.6023 - dense_1_loss_4: 0.2116 - dense_1_loss_5: 0.1332 - dense_1_loss_6: 0.0932 - dense_1_loss_7: 0.1028 - dense_1_loss_8: 0.0690 - dense_1_loss_9: 0.0603 - dense_1_loss_10: 0.0550 - dense_1_loss_11: 0.0536 - dense_1_loss_12: 0.0503 - dense_1_loss_13: 0.0458 - dense_1_loss_14: 0.0516 - dense_1_loss_15: 0.0536 - dense_1_loss_16: 0.0520 - dense_1_loss_17: 0.0531 - dense_1_loss_18: 0.0529 - dense_1_loss_19: 0.0530 - dense_1_loss_20: 0.0513 - dense_1_loss_21: 0.0569 - dense_1_loss_22: 0.0527 - dense_1_loss_23: 0.0525 - dense_1_loss_24: 0.0520 - dense_1_loss_25: 0.0552 - dense_1_loss_26: 0.0506 - dense_1_loss_27: 0.0563 - dense_1_loss_28: 0.0651 - dense_1_loss_29: 0.0691 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 78/100 60/60 [==============================] - 0s - loss: 7.6805 - dense_1_loss_1: 3.8263 - dense_1_loss_2: 1.5659 - dense_1_loss_3: 0.5914 - dense_1_loss_4: 0.2058 - dense_1_loss_5: 0.1287 - dense_1_loss_6: 0.0907 - dense_1_loss_7: 0.0985 - dense_1_loss_8: 0.0669 - dense_1_loss_9: 0.0582 - dense_1_loss_10: 0.0533 - dense_1_loss_11: 0.0516 - dense_1_loss_12: 0.0489 - dense_1_loss_13: 0.0445 - dense_1_loss_14: 0.0499 - dense_1_loss_15: 0.0519 - dense_1_loss_16: 0.0503 - dense_1_loss_17: 0.0514 - dense_1_loss_18: 0.0510 - dense_1_loss_19: 0.0514 - dense_1_loss_20: 0.0498 - dense_1_loss_21: 0.0548 - dense_1_loss_22: 0.0509 - dense_1_loss_23: 0.0508 - dense_1_loss_24: 0.0502 - dense_1_loss_25: 0.0536 - dense_1_loss_26: 0.0489 - dense_1_loss_27: 0.0548 - dense_1_loss_28: 0.0635 - dense_1_loss_29: 0.0668 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 79/100 60/60 [==============================] - 0s - loss: 7.5956 - dense_1_loss_1: 3.8238 - dense_1_loss_2: 1.5515 - dense_1_loss_3: 0.5789 - dense_1_loss_4: 0.1995 - dense_1_loss_5: 0.1242 - dense_1_loss_6: 0.0880 - dense_1_loss_7: 0.0935 - dense_1_loss_8: 0.0650 - dense_1_loss_9: 0.0563 - dense_1_loss_10: 0.0516 - dense_1_loss_11: 0.0501 - dense_1_loss_12: 0.0475 - dense_1_loss_13: 0.0431 - dense_1_loss_14: 0.0484 - dense_1_loss_15: 0.0504 - dense_1_loss_16: 0.0487 - dense_1_loss_17: 0.0500 - dense_1_loss_18: 0.0495 - dense_1_loss_19: 0.0495 - dense_1_loss_20: 0.0483 - dense_1_loss_21: 0.0530 - dense_1_loss_22: 0.0492 - dense_1_loss_23: 0.0495 - dense_1_loss_24: 0.0485 - dense_1_loss_25: 0.0525 - dense_1_loss_26: 0.0476 - dense_1_loss_27: 0.0531 - dense_1_loss_28: 0.0596 - dense_1_loss_29: 0.0645 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 80/100 60/60 [==============================] - 0s - loss: 7.5191 - dense_1_loss_1: 3.8210 - dense_1_loss_2: 1.5362 - dense_1_loss_3: 0.5694 - dense_1_loss_4: 0.1943 - dense_1_loss_5: 0.1208 - dense_1_loss_6: 0.0853 - dense_1_loss_7: 0.0908 - dense_1_loss_8: 0.0633 - dense_1_loss_9: 0.0548 - dense_1_loss_10: 0.0501 - dense_1_loss_11: 0.0486 - dense_1_loss_12: 0.0461 - dense_1_loss_13: 0.0419 - dense_1_loss_14: 0.0469 - dense_1_loss_15: 0.0489 - dense_1_loss_16: 0.0472 - dense_1_loss_17: 0.0486 - dense_1_loss_18: 0.0481 - dense_1_loss_19: 0.0479 - dense_1_loss_20: 0.0467 - dense_1_loss_21: 0.0512 - dense_1_loss_22: 0.0477 - dense_1_loss_23: 0.0483 - dense_1_loss_24: 0.0469 - dense_1_loss_25: 0.0516 - dense_1_loss_26: 0.0463 - dense_1_loss_27: 0.0516 - dense_1_loss_28: 0.0567 - dense_1_loss_29: 0.0622 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 81/100 60/60 [==============================] - 0s - loss: 7.4435 - dense_1_loss_1: 3.8183 - dense_1_loss_2: 1.5221 - dense_1_loss_3: 0.5590 - dense_1_loss_4: 0.1891 - dense_1_loss_5: 0.1168 - dense_1_loss_6: 0.0831 - dense_1_loss_7: 0.0864 - dense_1_loss_8: 0.0612 - dense_1_loss_9: 0.0532 - dense_1_loss_10: 0.0487 - dense_1_loss_11: 0.0471 - dense_1_loss_12: 0.0447 - dense_1_loss_13: 0.0407 - dense_1_loss_14: 0.0455 - dense_1_loss_15: 0.0474 - dense_1_loss_16: 0.0457 - dense_1_loss_17: 0.0470 - dense_1_loss_18: 0.0468 - dense_1_loss_19: 0.0465 - dense_1_loss_20: 0.0452 - dense_1_loss_21: 0.0497 - dense_1_loss_22: 0.0464 - dense_1_loss_23: 0.0465 - dense_1_loss_24: 0.0456 - dense_1_loss_25: 0.0495 - dense_1_loss_26: 0.0447 - dense_1_loss_27: 0.0501 - dense_1_loss_28: 0.0563 - dense_1_loss_29: 0.0602 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 82/100 60/60 [==============================] - 0s - loss: 7.3770 - dense_1_loss_1: 3.8156 - dense_1_loss_2: 1.5081 - dense_1_loss_3: 0.5494 - dense_1_loss_4: 0.1843 - dense_1_loss_5: 0.1137 - dense_1_loss_6: 0.0811 - dense_1_loss_7: 0.0838 - dense_1_loss_8: 0.0595 - dense_1_loss_9: 0.0518 - dense_1_loss_10: 0.0471 - dense_1_loss_11: 0.0457 - dense_1_loss_12: 0.0434 - dense_1_loss_13: 0.0397 - dense_1_loss_14: 0.0443 - dense_1_loss_15: 0.0458 - dense_1_loss_16: 0.0446 - dense_1_loss_17: 0.0453 - dense_1_loss_18: 0.0454 - dense_1_loss_19: 0.0455 - dense_1_loss_20: 0.0438 - dense_1_loss_21: 0.0483 - dense_1_loss_22: 0.0449 - dense_1_loss_23: 0.0448 - dense_1_loss_24: 0.0446 - dense_1_loss_25: 0.0473 - dense_1_loss_26: 0.0429 - dense_1_loss_27: 0.0489 - dense_1_loss_28: 0.0586 - dense_1_loss_29: 0.0588 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 83/100 60/60 [==============================] - 0s - loss: 7.3022 - dense_1_loss_1: 3.8131 - dense_1_loss_2: 1.4937 - dense_1_loss_3: 0.5394 - dense_1_loss_4: 0.1790 - dense_1_loss_5: 0.1103 - dense_1_loss_6: 0.0789 - dense_1_loss_7: 0.0798 - dense_1_loss_8: 0.0577 - dense_1_loss_9: 0.0503 - dense_1_loss_10: 0.0459 - dense_1_loss_11: 0.0443 - dense_1_loss_12: 0.0422 - dense_1_loss_13: 0.0386 - dense_1_loss_14: 0.0430 - dense_1_loss_15: 0.0446 - dense_1_loss_16: 0.0433 - dense_1_loss_17: 0.0441 - dense_1_loss_18: 0.0441 - dense_1_loss_19: 0.0441 - dense_1_loss_20: 0.0425 - dense_1_loss_21: 0.0468 - dense_1_loss_22: 0.0436 - dense_1_loss_23: 0.0437 - dense_1_loss_24: 0.0432 - dense_1_loss_25: 0.0463 - dense_1_loss_26: 0.0417 - dense_1_loss_27: 0.0473 - dense_1_loss_28: 0.0542 - dense_1_loss_29: 0.0566 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 84/100 60/60 [==============================] - 0s - loss: 7.2371 - dense_1_loss_1: 3.8105 - dense_1_loss_2: 1.4800 - dense_1_loss_3: 0.5300 - dense_1_loss_4: 0.1751 - dense_1_loss_5: 0.1075 - dense_1_loss_6: 0.0766 - dense_1_loss_7: 0.0769 - dense_1_loss_8: 0.0564 - dense_1_loss_9: 0.0489 - dense_1_loss_10: 0.0447 - dense_1_loss_11: 0.0433 - dense_1_loss_12: 0.0409 - dense_1_loss_13: 0.0375 - dense_1_loss_14: 0.0419 - dense_1_loss_15: 0.0435 - dense_1_loss_16: 0.0421 - dense_1_loss_17: 0.0429 - dense_1_loss_18: 0.0428 - dense_1_loss_19: 0.0427 - dense_1_loss_20: 0.0413 - dense_1_loss_21: 0.0456 - dense_1_loss_22: 0.0423 - dense_1_loss_23: 0.0427 - dense_1_loss_24: 0.0420 - dense_1_loss_25: 0.0457 - dense_1_loss_26: 0.0410 - dense_1_loss_27: 0.0461 - dense_1_loss_28: 0.0506 - dense_1_loss_29: 0.0555 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 85/100 60/60 [==============================] - 0s - loss: 7.1763 - dense_1_loss_1: 3.8078 - dense_1_loss_2: 1.4673 - dense_1_loss_3: 0.5201 - dense_1_loss_4: 0.1708 - dense_1_loss_5: 0.1046 - dense_1_loss_6: 0.0747 - dense_1_loss_7: 0.0740 - dense_1_loss_8: 0.0550 - dense_1_loss_9: 0.0476 - dense_1_loss_10: 0.0436 - dense_1_loss_11: 0.0420 - dense_1_loss_12: 0.0400 - dense_1_loss_13: 0.0367 - dense_1_loss_14: 0.0407 - dense_1_loss_15: 0.0425 - dense_1_loss_16: 0.0409 - dense_1_loss_17: 0.0421 - dense_1_loss_18: 0.0417 - dense_1_loss_19: 0.0414 - dense_1_loss_20: 0.0403 - dense_1_loss_21: 0.0442 - dense_1_loss_22: 0.0411 - dense_1_loss_23: 0.0420 - dense_1_loss_24: 0.0407 - dense_1_loss_25: 0.0456 - dense_1_loss_26: 0.0408 - dense_1_loss_27: 0.0450 - dense_1_loss_28: 0.0483 - dense_1_loss_29: 0.0546 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 86/100 60/60 [==============================] - 0s - loss: 7.1128 - dense_1_loss_1: 3.8055 - dense_1_loss_2: 1.4544 - dense_1_loss_3: 0.5110 - dense_1_loss_4: 0.1668 - dense_1_loss_5: 0.1018 - dense_1_loss_6: 0.0733 - dense_1_loss_7: 0.0713 - dense_1_loss_8: 0.0536 - dense_1_loss_9: 0.0464 - dense_1_loss_10: 0.0425 - dense_1_loss_11: 0.0409 - dense_1_loss_12: 0.0390 - dense_1_loss_13: 0.0357 - dense_1_loss_14: 0.0396 - dense_1_loss_15: 0.0412 - dense_1_loss_16: 0.0399 - dense_1_loss_17: 0.0408 - dense_1_loss_18: 0.0406 - dense_1_loss_19: 0.0407 - dense_1_loss_20: 0.0391 - dense_1_loss_21: 0.0429 - dense_1_loss_22: 0.0401 - dense_1_loss_23: 0.0402 - dense_1_loss_24: 0.0397 - dense_1_loss_25: 0.0431 - dense_1_loss_26: 0.0386 - dense_1_loss_27: 0.0439 - dense_1_loss_28: 0.0481 - dense_1_loss_29: 0.0521 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 87/100 60/60 [==============================] - 0s - loss: 7.0580 - dense_1_loss_1: 3.8027 - dense_1_loss_2: 1.4423 - dense_1_loss_3: 0.5028 - dense_1_loss_4: 0.1629 - dense_1_loss_5: 0.0995 - dense_1_loss_6: 0.0715 - dense_1_loss_7: 0.0694 - dense_1_loss_8: 0.0522 - dense_1_loss_9: 0.0453 - dense_1_loss_10: 0.0414 - dense_1_loss_11: 0.0396 - dense_1_loss_12: 0.0380 - dense_1_loss_13: 0.0349 - dense_1_loss_14: 0.0386 - dense_1_loss_15: 0.0400 - dense_1_loss_16: 0.0389 - dense_1_loss_17: 0.0395 - dense_1_loss_18: 0.0395 - dense_1_loss_19: 0.0399 - dense_1_loss_20: 0.0380 - dense_1_loss_21: 0.0417 - dense_1_loss_22: 0.0390 - dense_1_loss_23: 0.0388 - dense_1_loss_24: 0.0390 - dense_1_loss_25: 0.0415 - dense_1_loss_26: 0.0373 - dense_1_loss_27: 0.0430 - dense_1_loss_28: 0.0500 - dense_1_loss_29: 0.0509 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 88/100 60/60 [==============================] - 0s - loss: 6.9982 - dense_1_loss_1: 3.8001 - dense_1_loss_2: 1.4298 - dense_1_loss_3: 0.4939 - dense_1_loss_4: 0.1594 - dense_1_loss_5: 0.0969 - dense_1_loss_6: 0.0697 - dense_1_loss_7: 0.0667 - dense_1_loss_8: 0.0509 - dense_1_loss_9: 0.0443 - dense_1_loss_10: 0.0405 - dense_1_loss_11: 0.0387 - dense_1_loss_12: 0.0369 - dense_1_loss_13: 0.0340 - dense_1_loss_14: 0.0377 - dense_1_loss_15: 0.0390 - dense_1_loss_16: 0.0378 - dense_1_loss_17: 0.0385 - dense_1_loss_18: 0.0386 - dense_1_loss_19: 0.0387 - dense_1_loss_20: 0.0370 - dense_1_loss_21: 0.0406 - dense_1_loss_22: 0.0379 - dense_1_loss_23: 0.0380 - dense_1_loss_24: 0.0379 - dense_1_loss_25: 0.0406 - dense_1_loss_26: 0.0365 - dense_1_loss_27: 0.0417 - dense_1_loss_28: 0.0467 - dense_1_loss_29: 0.0493 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 89/100 60/60 [==============================] - 0s - loss: 6.9425 - dense_1_loss_1: 3.7977 - dense_1_loss_2: 1.4176 - dense_1_loss_3: 0.4855 - dense_1_loss_4: 0.1556 - dense_1_loss_5: 0.0945 - dense_1_loss_6: 0.0678 - dense_1_loss_7: 0.0638 - dense_1_loss_8: 0.0498 - dense_1_loss_9: 0.0431 - dense_1_loss_10: 0.0394 - dense_1_loss_11: 0.0378 - dense_1_loss_12: 0.0359 - dense_1_loss_13: 0.0332 - dense_1_loss_14: 0.0369 - dense_1_loss_15: 0.0380 - dense_1_loss_16: 0.0368 - dense_1_loss_17: 0.0376 - dense_1_loss_18: 0.0376 - dense_1_loss_19: 0.0376 - dense_1_loss_20: 0.0361 - dense_1_loss_21: 0.0396 - dense_1_loss_22: 0.0370 - dense_1_loss_23: 0.0372 - dense_1_loss_24: 0.0370 - dense_1_loss_25: 0.0401 - dense_1_loss_26: 0.0358 - dense_1_loss_27: 0.0407 - dense_1_loss_28: 0.0443 - dense_1_loss_29: 0.0485 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 90/100 60/60 [==============================] - 0s - loss: 6.8932 - dense_1_loss_1: 3.7952 - dense_1_loss_2: 1.4060 - dense_1_loss_3: 0.4778 - dense_1_loss_4: 0.1522 - dense_1_loss_5: 0.0926 - dense_1_loss_6: 0.0662 - dense_1_loss_7: 0.0622 - dense_1_loss_8: 0.0487 - dense_1_loss_9: 0.0421 - dense_1_loss_10: 0.0384 - dense_1_loss_11: 0.0371 - dense_1_loss_12: 0.0351 - dense_1_loss_13: 0.0325 - dense_1_loss_14: 0.0360 - dense_1_loss_15: 0.0372 - dense_1_loss_16: 0.0359 - dense_1_loss_17: 0.0367 - dense_1_loss_18: 0.0367 - dense_1_loss_19: 0.0366 - dense_1_loss_20: 0.0353 - dense_1_loss_21: 0.0387 - dense_1_loss_22: 0.0361 - dense_1_loss_23: 0.0367 - dense_1_loss_24: 0.0360 - dense_1_loss_25: 0.0399 - dense_1_loss_26: 0.0356 - dense_1_loss_27: 0.0399 - dense_1_loss_28: 0.0422 - dense_1_loss_29: 0.0479 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 91/100 60/60 [==============================] - 0s - loss: 6.8411 - dense_1_loss_1: 3.7928 - dense_1_loss_2: 1.3946 - dense_1_loss_3: 0.4706 - dense_1_loss_4: 0.1489 - dense_1_loss_5: 0.0904 - dense_1_loss_6: 0.0647 - dense_1_loss_7: 0.0601 - dense_1_loss_8: 0.0476 - dense_1_loss_9: 0.0411 - dense_1_loss_10: 0.0374 - dense_1_loss_11: 0.0361 - dense_1_loss_12: 0.0343 - dense_1_loss_13: 0.0316 - dense_1_loss_14: 0.0349 - dense_1_loss_15: 0.0363 - dense_1_loss_16: 0.0352 - dense_1_loss_17: 0.0358 - dense_1_loss_18: 0.0358 - dense_1_loss_19: 0.0359 - dense_1_loss_20: 0.0344 - dense_1_loss_21: 0.0377 - dense_1_loss_22: 0.0352 - dense_1_loss_23: 0.0354 - dense_1_loss_24: 0.0352 - dense_1_loss_25: 0.0383 - dense_1_loss_26: 0.0342 - dense_1_loss_27: 0.0388 - dense_1_loss_28: 0.0416 - dense_1_loss_29: 0.0461 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 92/100 60/60 [==============================] - 0s - loss: 6.7922 - dense_1_loss_1: 3.7902 - dense_1_loss_2: 1.3833 - dense_1_loss_3: 0.4623 - dense_1_loss_4: 0.1455 - dense_1_loss_5: 0.0884 - dense_1_loss_6: 0.0633 - dense_1_loss_7: 0.0582 - dense_1_loss_8: 0.0465 - dense_1_loss_9: 0.0401 - dense_1_loss_10: 0.0366 - dense_1_loss_11: 0.0352 - dense_1_loss_12: 0.0336 - dense_1_loss_13: 0.0310 - dense_1_loss_14: 0.0340 - dense_1_loss_15: 0.0355 - dense_1_loss_16: 0.0344 - dense_1_loss_17: 0.0348 - dense_1_loss_18: 0.0348 - dense_1_loss_19: 0.0352 - dense_1_loss_20: 0.0335 - dense_1_loss_21: 0.0366 - dense_1_loss_22: 0.0343 - dense_1_loss_23: 0.0342 - dense_1_loss_24: 0.0345 - dense_1_loss_25: 0.0370 - dense_1_loss_26: 0.0330 - dense_1_loss_27: 0.0380 - dense_1_loss_28: 0.0433 - dense_1_loss_29: 0.0448 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 93/100 60/60 [==============================] - 0s - loss: 6.7446 - dense_1_loss_1: 3.7877 - dense_1_loss_2: 1.3725 - dense_1_loss_3: 0.4552 - dense_1_loss_4: 0.1427 - dense_1_loss_5: 0.0866 - dense_1_loss_6: 0.0618 - dense_1_loss_7: 0.0564 - dense_1_loss_8: 0.0456 - dense_1_loss_9: 0.0391 - dense_1_loss_10: 0.0358 - dense_1_loss_11: 0.0344 - dense_1_loss_12: 0.0328 - dense_1_loss_13: 0.0303 - dense_1_loss_14: 0.0332 - dense_1_loss_15: 0.0348 - dense_1_loss_16: 0.0336 - dense_1_loss_17: 0.0340 - dense_1_loss_18: 0.0340 - dense_1_loss_19: 0.0344 - dense_1_loss_20: 0.0327 - dense_1_loss_21: 0.0357 - dense_1_loss_22: 0.0335 - dense_1_loss_23: 0.0335 - dense_1_loss_24: 0.0337 - dense_1_loss_25: 0.0362 - dense_1_loss_26: 0.0322 - dense_1_loss_27: 0.0371 - dense_1_loss_28: 0.0414 - dense_1_loss_29: 0.0436 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 94/100 60/60 [==============================] - 0s - loss: 6.6988 - dense_1_loss_1: 3.7855 - dense_1_loss_2: 1.3616 - dense_1_loss_3: 0.4477 - dense_1_loss_4: 0.1401 - dense_1_loss_5: 0.0848 - dense_1_loss_6: 0.0606 - dense_1_loss_7: 0.0548 - dense_1_loss_8: 0.0447 - dense_1_loss_9: 0.0382 - dense_1_loss_10: 0.0351 - dense_1_loss_11: 0.0337 - dense_1_loss_12: 0.0320 - dense_1_loss_13: 0.0296 - dense_1_loss_14: 0.0324 - dense_1_loss_15: 0.0340 - dense_1_loss_16: 0.0328 - dense_1_loss_17: 0.0334 - dense_1_loss_18: 0.0333 - dense_1_loss_19: 0.0335 - dense_1_loss_20: 0.0320 - dense_1_loss_21: 0.0349 - dense_1_loss_22: 0.0327 - dense_1_loss_23: 0.0330 - dense_1_loss_24: 0.0328 - dense_1_loss_25: 0.0359 - dense_1_loss_26: 0.0319 - dense_1_loss_27: 0.0363 - dense_1_loss_28: 0.0386 - dense_1_loss_29: 0.0428 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 95/100 60/60 [==============================] - 0s - loss: 6.6540 - dense_1_loss_1: 3.7830 - dense_1_loss_2: 1.3508 - dense_1_loss_3: 0.4403 - dense_1_loss_4: 0.1371 - dense_1_loss_5: 0.0832 - dense_1_loss_6: 0.0591 - dense_1_loss_7: 0.0529 - dense_1_loss_8: 0.0438 - dense_1_loss_9: 0.0373 - dense_1_loss_10: 0.0343 - dense_1_loss_11: 0.0330 - dense_1_loss_12: 0.0312 - dense_1_loss_13: 0.0290 - dense_1_loss_14: 0.0318 - dense_1_loss_15: 0.0332 - dense_1_loss_16: 0.0321 - dense_1_loss_17: 0.0326 - dense_1_loss_18: 0.0326 - dense_1_loss_19: 0.0327 - dense_1_loss_20: 0.0313 - dense_1_loss_21: 0.0342 - dense_1_loss_22: 0.0320 - dense_1_loss_23: 0.0324 - dense_1_loss_24: 0.0321 - dense_1_loss_25: 0.0352 - dense_1_loss_26: 0.0314 - dense_1_loss_27: 0.0356 - dense_1_loss_28: 0.0377 - dense_1_loss_29: 0.0421 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 96/100 60/60 [==============================] - 0s - loss: 6.6105 - dense_1_loss_1: 3.7806 - dense_1_loss_2: 1.3401 - dense_1_loss_3: 0.4335 - dense_1_loss_4: 0.1345 - dense_1_loss_5: 0.0816 - dense_1_loss_6: 0.0581 - dense_1_loss_7: 0.0517 - dense_1_loss_8: 0.0429 - dense_1_loss_9: 0.0366 - dense_1_loss_10: 0.0336 - dense_1_loss_11: 0.0323 - dense_1_loss_12: 0.0305 - dense_1_loss_13: 0.0284 - dense_1_loss_14: 0.0311 - dense_1_loss_15: 0.0324 - dense_1_loss_16: 0.0314 - dense_1_loss_17: 0.0317 - dense_1_loss_18: 0.0319 - dense_1_loss_19: 0.0321 - dense_1_loss_20: 0.0306 - dense_1_loss_21: 0.0334 - dense_1_loss_22: 0.0312 - dense_1_loss_23: 0.0314 - dense_1_loss_24: 0.0315 - dense_1_loss_25: 0.0340 - dense_1_loss_26: 0.0303 - dense_1_loss_27: 0.0348 - dense_1_loss_28: 0.0376 - dense_1_loss_29: 0.0407 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 97/100 60/60 [==============================] - 0s - loss: 6.5674 - dense_1_loss_1: 3.7782 - dense_1_loss_2: 1.3294 - dense_1_loss_3: 0.4264 - dense_1_loss_4: 0.1319 - dense_1_loss_5: 0.0797 - dense_1_loss_6: 0.0570 - dense_1_loss_7: 0.0500 - dense_1_loss_8: 0.0419 - dense_1_loss_9: 0.0359 - dense_1_loss_10: 0.0328 - dense_1_loss_11: 0.0315 - dense_1_loss_12: 0.0299 - dense_1_loss_13: 0.0278 - dense_1_loss_14: 0.0304 - dense_1_loss_15: 0.0316 - dense_1_loss_16: 0.0308 - dense_1_loss_17: 0.0310 - dense_1_loss_18: 0.0313 - dense_1_loss_19: 0.0314 - dense_1_loss_20: 0.0299 - dense_1_loss_21: 0.0327 - dense_1_loss_22: 0.0307 - dense_1_loss_23: 0.0307 - dense_1_loss_24: 0.0308 - dense_1_loss_25: 0.0332 - dense_1_loss_26: 0.0297 - dense_1_loss_27: 0.0342 - dense_1_loss_28: 0.0370 - dense_1_loss_29: 0.0398 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 98/100 60/60 [==============================] - 0s - loss: 6.5286 - dense_1_loss_1: 3.7757 - dense_1_loss_2: 1.3199 - dense_1_loss_3: 0.4204 - dense_1_loss_4: 0.1295 - dense_1_loss_5: 0.0782 - dense_1_loss_6: 0.0559 - dense_1_loss_7: 0.0489 - dense_1_loss_8: 0.0411 - dense_1_loss_9: 0.0352 - dense_1_loss_10: 0.0321 - dense_1_loss_11: 0.0309 - dense_1_loss_12: 0.0293 - dense_1_loss_13: 0.0272 - dense_1_loss_14: 0.0298 - dense_1_loss_15: 0.0310 - dense_1_loss_16: 0.0302 - dense_1_loss_17: 0.0303 - dense_1_loss_18: 0.0306 - dense_1_loss_19: 0.0308 - dense_1_loss_20: 0.0292 - dense_1_loss_21: 0.0321 - dense_1_loss_22: 0.0300 - dense_1_loss_23: 0.0300 - dense_1_loss_24: 0.0302 - dense_1_loss_25: 0.0325 - dense_1_loss_26: 0.0290 - dense_1_loss_27: 0.0335 - dense_1_loss_28: 0.0362 - dense_1_loss_29: 0.0389 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 99/100 60/60 [==============================] - 0s - loss: 6.4894 - dense_1_loss_1: 3.7733 - dense_1_loss_2: 1.3097 - dense_1_loss_3: 0.4140 - dense_1_loss_4: 0.1274 - dense_1_loss_5: 0.0768 - dense_1_loss_6: 0.0549 - dense_1_loss_7: 0.0477 - dense_1_loss_8: 0.0403 - dense_1_loss_9: 0.0345 - dense_1_loss_10: 0.0315 - dense_1_loss_11: 0.0302 - dense_1_loss_12: 0.0287 - dense_1_loss_13: 0.0267 - dense_1_loss_14: 0.0291 - dense_1_loss_15: 0.0304 - dense_1_loss_16: 0.0295 - dense_1_loss_17: 0.0297 - dense_1_loss_18: 0.0300 - dense_1_loss_19: 0.0302 - dense_1_loss_20: 0.0286 - dense_1_loss_21: 0.0314 - dense_1_loss_22: 0.0293 - dense_1_loss_23: 0.0294 - dense_1_loss_24: 0.0296 - dense_1_loss_25: 0.0319 - dense_1_loss_26: 0.0285 - dense_1_loss_27: 0.0328 - dense_1_loss_28: 0.0350 - dense_1_loss_29: 0.0382 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167 Epoch 100/100 60/60 [==============================] - 0s - loss: 6.4512 - dense_1_loss_1: 3.7711 - dense_1_loss_2: 1.3001 - dense_1_loss_3: 0.4076 - dense_1_loss_4: 0.1252 - dense_1_loss_5: 0.0754 - dense_1_loss_6: 0.0538 - dense_1_loss_7: 0.0465 - dense_1_loss_8: 0.0395 - dense_1_loss_9: 0.0337 - dense_1_loss_10: 0.0309 - dense_1_loss_11: 0.0296 - dense_1_loss_12: 0.0282 - dense_1_loss_13: 0.0261 - dense_1_loss_14: 0.0285 - dense_1_loss_15: 0.0298 - dense_1_loss_16: 0.0289 - dense_1_loss_17: 0.0291 - dense_1_loss_18: 0.0294 - dense_1_loss_19: 0.0295 - dense_1_loss_20: 0.0281 - dense_1_loss_21: 0.0307 - dense_1_loss_22: 0.0287 - dense_1_loss_23: 0.0289 - dense_1_loss_24: 0.0290 - dense_1_loss_25: 0.0314 - dense_1_loss_26: 0.0279 - dense_1_loss_27: 0.0321 - dense_1_loss_28: 0.0340 - dense_1_loss_29: 0.0375 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0167
<keras.callbacks.History at 0x7f65bf118d68>
The model loss will start high, (100 or so), and after 100 epochs, it should be in the single digits. These won't be the exact number that you'll see, due to random initialization of weights.
For example:
Epoch 1/100
60/60 [==============================] - 3s - loss: 125.7673
...
Scroll to the bottom to check Epoch 100
...
Epoch 100/100
60/60 [==============================] - 0s - loss: 6.1861
Now that you have trained a model, let's go to the final section to implement an inference algorithm, and generate some music!
You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music.
At each step of sampling, you will:
a
' and cell state 'c
' from the previous state of the LSTM.a
' can then be used to generate the output using the fully connected layer, densor
. x0
a0
c0
Exercise:
Here are some of the key steps you'll need to implement inside the for-loop that generates the $T_y$ output characters:
Step 2.A: Use LSTM_Cell
, which takes in the input layer, as well as the previous step's 'c
' and 'a
' to generate the current step's 'c
' and 'a
'.
next_hidden_state, _, next_cell_state = LSTM_cell(input_x, initial_state=[previous_hidden_state, previous_cell_state])
Step 2.B: Compute the output by applying densor
to compute a softmax on 'a
' to get the output for the current step.
Step 2.C: Append the output to the list outputs
.
out
'. one_hot(x)
in the 'music_utils.py' file and imported it.
Here is the definition of one_hot
def one_hot(x):
x = K.argmax(x)
x = tf.one_hot(indices=x, depth=78)
x = RepeatVector(1)(x)
return x
one_hot
function is doing:x
, find the position with the maximum value and return the index of that position. n
times. Notice that we had it repeat 1 time. This may seem like it's not doing anything. If you look at the documentation for RepeatVector, you'll notice that if x is a vector with dimension (m,5) and it gets passed into RepeatVector(1)
, then the output is (m,1,5). In other words, it adds an additional dimension (of length 1) to the resulting vector.result = Lambda(lambda x: x + 1)(input_var)
If you pre-define a function, you can do the same thing:
def add_one(x)
return x + 1
# use the add_one function inside of the Lambda function
result = Lambda(add_one)(input_var)
This is how to use the Keras Model
.
model = Model(inputs=[input_x, initial_hidden_state, initial_cell_state], outputs=the_outputs)
# GRADED FUNCTION: music_inference_model
def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):
"""
Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.
Arguments:
LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
densor -- the trained "densor" from model(), Keras layer object
n_values -- integer, number of unique values
n_a -- number of units in the LSTM_cell
Ty -- integer, number of time steps to generate
Returns:
inference_model -- Keras model instance
"""
# Define the input of your model with a shape
x0 = Input(shape=(1, n_values))
# Define s0, initial hidden state for the decoder LSTM
a0 = Input(shape=(n_a,), name='a0')
c0 = Input(shape=(n_a,), name='c0')
a = a0
c = c0
x = x0
### START CODE HERE ###
# Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
outputs = []
# Step 2: Loop over Ty and generate a value at every time step
for t in range(Ty):
# Step 2.A: Perform one step of LSTM_cell (≈1 line)
a, _, c = LSTM_cell(x, initial_state=[a, c])
# Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
out = densor(a)
# Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 78) (≈1 line)
outputs.append(out)
# Step 2.D:
# Select the next value according to "out",
# Set "x" to be the one-hot representation of the selected value
# See instructions above.
x = Lambda(one_hot)(out)
# Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
inference_model = Model(inputs = [x0, a0, c0], outputs = outputs)
### END CODE HERE ###
return inference_model
Run the cell below to define your inference model. This model is hard coded to generate 50 values.
inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)
# Check the inference model
inference_model.summary()
____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_2 (InputLayer) (None, 1, 78) 0 ____________________________________________________________________________________________________ a0 (InputLayer) (None, 64) 0 ____________________________________________________________________________________________________ c0 (InputLayer) (None, 64) 0 ____________________________________________________________________________________________________ lstm_1 (LSTM) [(None, 64), (None, 6 36608 input_2[0][0] a0[0][0] c0[0][0] lambda_31[0][0] lstm_1[30][0] lstm_1[30][2] lambda_32[0][0] lstm_1[31][0] lstm_1[31][2] lambda_33[0][0] lstm_1[32][0] lstm_1[32][2] lambda_34[0][0] lstm_1[33][0] lstm_1[33][2] lambda_35[0][0] lstm_1[34][0] lstm_1[34][2] lambda_36[0][0] lstm_1[35][0] lstm_1[35][2] lambda_37[0][0] lstm_1[36][0] lstm_1[36][2] lambda_38[0][0] lstm_1[37][0] lstm_1[37][2] lambda_39[0][0] lstm_1[38][0] lstm_1[38][2] lambda_40[0][0] lstm_1[39][0] lstm_1[39][2] lambda_41[0][0] lstm_1[40][0] lstm_1[40][2] lambda_42[0][0] lstm_1[41][0] lstm_1[41][2] lambda_43[0][0] lstm_1[42][0] lstm_1[42][2] lambda_44[0][0] lstm_1[43][0] lstm_1[43][2] lambda_45[0][0] lstm_1[44][0] lstm_1[44][2] lambda_46[0][0] lstm_1[45][0] lstm_1[45][2] lambda_47[0][0] lstm_1[46][0] lstm_1[46][2] lambda_48[0][0] lstm_1[47][0] lstm_1[47][2] lambda_49[0][0] lstm_1[48][0] lstm_1[48][2] lambda_50[0][0] lstm_1[49][0] lstm_1[49][2] lambda_51[0][0] lstm_1[50][0] lstm_1[50][2] lambda_52[0][0] lstm_1[51][0] lstm_1[51][2] lambda_53[0][0] lstm_1[52][0] lstm_1[52][2] lambda_54[0][0] lstm_1[53][0] lstm_1[53][2] lambda_55[0][0] lstm_1[54][0] lstm_1[54][2] lambda_56[0][0] lstm_1[55][0] lstm_1[55][2] lambda_57[0][0] lstm_1[56][0] lstm_1[56][2] lambda_58[0][0] lstm_1[57][0] lstm_1[57][2] lambda_59[0][0] lstm_1[58][0] lstm_1[58][2] lambda_60[0][0] lstm_1[59][0] lstm_1[59][2] lambda_61[0][0] lstm_1[60][0] lstm_1[60][2] lambda_62[0][0] lstm_1[61][0] lstm_1[61][2] lambda_63[0][0] lstm_1[62][0] lstm_1[62][2] lambda_64[0][0] lstm_1[63][0] lstm_1[63][2] lambda_65[0][0] lstm_1[64][0] lstm_1[64][2] lambda_66[0][0] lstm_1[65][0] lstm_1[65][2] lambda_67[0][0] lstm_1[66][0] lstm_1[66][2] lambda_68[0][0] lstm_1[67][0] lstm_1[67][2] lambda_69[0][0] lstm_1[68][0] lstm_1[68][2] lambda_70[0][0] lstm_1[69][0] lstm_1[69][2] lambda_71[0][0] lstm_1[70][0] lstm_1[70][2] lambda_72[0][0] lstm_1[71][0] lstm_1[71][2] lambda_73[0][0] lstm_1[72][0] lstm_1[72][2] lambda_74[0][0] lstm_1[73][0] lstm_1[73][2] lambda_75[0][0] lstm_1[74][0] lstm_1[74][2] lambda_76[0][0] lstm_1[75][0] lstm_1[75][2] lambda_77[0][0] lstm_1[76][0] lstm_1[76][2] lambda_78[0][0] lstm_1[77][0] lstm_1[77][2] lambda_79[0][0] lstm_1[78][0] lstm_1[78][2] ____________________________________________________________________________________________________ dense_1 (Dense) (None, 78) 5070 lstm_1[30][0] lstm_1[31][0] lstm_1[32][0] lstm_1[33][0] lstm_1[34][0] lstm_1[35][0] lstm_1[36][0] lstm_1[37][0] lstm_1[38][0] lstm_1[39][0] lstm_1[40][0] lstm_1[41][0] lstm_1[42][0] lstm_1[43][0] lstm_1[44][0] lstm_1[45][0] lstm_1[46][0] lstm_1[47][0] lstm_1[48][0] lstm_1[49][0] lstm_1[50][0] lstm_1[51][0] lstm_1[52][0] lstm_1[53][0] lstm_1[54][0] lstm_1[55][0] lstm_1[56][0] lstm_1[57][0] lstm_1[58][0] lstm_1[59][0] lstm_1[60][0] lstm_1[61][0] lstm_1[62][0] lstm_1[63][0] lstm_1[64][0] lstm_1[65][0] lstm_1[66][0] lstm_1[67][0] lstm_1[68][0] lstm_1[69][0] lstm_1[70][0] lstm_1[71][0] lstm_1[72][0] lstm_1[73][0] lstm_1[74][0] lstm_1[75][0] lstm_1[76][0] lstm_1[77][0] lstm_1[78][0] lstm_1[79][0] ____________________________________________________________________________________________________ lambda_31 (Lambda) (None, 1, 78) 0 dense_1[30][0] ____________________________________________________________________________________________________ lambda_32 (Lambda) (None, 1, 78) 0 dense_1[31][0] ____________________________________________________________________________________________________ lambda_33 (Lambda) (None, 1, 78) 0 dense_1[32][0] ____________________________________________________________________________________________________ lambda_34 (Lambda) (None, 1, 78) 0 dense_1[33][0] ____________________________________________________________________________________________________ lambda_35 (Lambda) (None, 1, 78) 0 dense_1[34][0] ____________________________________________________________________________________________________ lambda_36 (Lambda) (None, 1, 78) 0 dense_1[35][0] ____________________________________________________________________________________________________ lambda_37 (Lambda) (None, 1, 78) 0 dense_1[36][0] ____________________________________________________________________________________________________ lambda_38 (Lambda) (None, 1, 78) 0 dense_1[37][0] ____________________________________________________________________________________________________ lambda_39 (Lambda) (None, 1, 78) 0 dense_1[38][0] ____________________________________________________________________________________________________ lambda_40 (Lambda) (None, 1, 78) 0 dense_1[39][0] ____________________________________________________________________________________________________ lambda_41 (Lambda) (None, 1, 78) 0 dense_1[40][0] ____________________________________________________________________________________________________ lambda_42 (Lambda) (None, 1, 78) 0 dense_1[41][0] ____________________________________________________________________________________________________ lambda_43 (Lambda) (None, 1, 78) 0 dense_1[42][0] ____________________________________________________________________________________________________ lambda_44 (Lambda) (None, 1, 78) 0 dense_1[43][0] ____________________________________________________________________________________________________ lambda_45 (Lambda) (None, 1, 78) 0 dense_1[44][0] ____________________________________________________________________________________________________ lambda_46 (Lambda) (None, 1, 78) 0 dense_1[45][0] ____________________________________________________________________________________________________ lambda_47 (Lambda) (None, 1, 78) 0 dense_1[46][0] ____________________________________________________________________________________________________ lambda_48 (Lambda) (None, 1, 78) 0 dense_1[47][0] ____________________________________________________________________________________________________ lambda_49 (Lambda) (None, 1, 78) 0 dense_1[48][0] ____________________________________________________________________________________________________ lambda_50 (Lambda) (None, 1, 78) 0 dense_1[49][0] ____________________________________________________________________________________________________ lambda_51 (Lambda) (None, 1, 78) 0 dense_1[50][0] ____________________________________________________________________________________________________ lambda_52 (Lambda) (None, 1, 78) 0 dense_1[51][0] ____________________________________________________________________________________________________ lambda_53 (Lambda) (None, 1, 78) 0 dense_1[52][0] ____________________________________________________________________________________________________ lambda_54 (Lambda) (None, 1, 78) 0 dense_1[53][0] ____________________________________________________________________________________________________ lambda_55 (Lambda) (None, 1, 78) 0 dense_1[54][0] ____________________________________________________________________________________________________ lambda_56 (Lambda) (None, 1, 78) 0 dense_1[55][0] ____________________________________________________________________________________________________ lambda_57 (Lambda) (None, 1, 78) 0 dense_1[56][0] ____________________________________________________________________________________________________ lambda_58 (Lambda) (None, 1, 78) 0 dense_1[57][0] ____________________________________________________________________________________________________ lambda_59 (Lambda) (None, 1, 78) 0 dense_1[58][0] ____________________________________________________________________________________________________ lambda_60 (Lambda) (None, 1, 78) 0 dense_1[59][0] ____________________________________________________________________________________________________ lambda_61 (Lambda) (None, 1, 78) 0 dense_1[60][0] ____________________________________________________________________________________________________ lambda_62 (Lambda) (None, 1, 78) 0 dense_1[61][0] ____________________________________________________________________________________________________ lambda_63 (Lambda) (None, 1, 78) 0 dense_1[62][0] ____________________________________________________________________________________________________ lambda_64 (Lambda) (None, 1, 78) 0 dense_1[63][0] ____________________________________________________________________________________________________ lambda_65 (Lambda) (None, 1, 78) 0 dense_1[64][0] ____________________________________________________________________________________________________ lambda_66 (Lambda) (None, 1, 78) 0 dense_1[65][0] ____________________________________________________________________________________________________ lambda_67 (Lambda) (None, 1, 78) 0 dense_1[66][0] ____________________________________________________________________________________________________ lambda_68 (Lambda) (None, 1, 78) 0 dense_1[67][0] ____________________________________________________________________________________________________ lambda_69 (Lambda) (None, 1, 78) 0 dense_1[68][0] ____________________________________________________________________________________________________ lambda_70 (Lambda) (None, 1, 78) 0 dense_1[69][0] ____________________________________________________________________________________________________ lambda_71 (Lambda) (None, 1, 78) 0 dense_1[70][0] ____________________________________________________________________________________________________ lambda_72 (Lambda) (None, 1, 78) 0 dense_1[71][0] ____________________________________________________________________________________________________ lambda_73 (Lambda) (None, 1, 78) 0 dense_1[72][0] ____________________________________________________________________________________________________ lambda_74 (Lambda) (None, 1, 78) 0 dense_1[73][0] ____________________________________________________________________________________________________ lambda_75 (Lambda) (None, 1, 78) 0 dense_1[74][0] ____________________________________________________________________________________________________ lambda_76 (Lambda) (None, 1, 78) 0 dense_1[75][0] ____________________________________________________________________________________________________ lambda_77 (Lambda) (None, 1, 78) 0 dense_1[76][0] ____________________________________________________________________________________________________ lambda_78 (Lambda) (None, 1, 78) 0 dense_1[77][0] ____________________________________________________________________________________________________ lambda_79 (Lambda) (None, 1, 78) 0 dense_1[78][0] ==================================================================================================== Total params: 41,678 Trainable params: 41,678 Non-trainable params: 0 ____________________________________________________________________________________________________
Expected Output If you scroll to the bottom of the output, you'll see:
Total params: 41,678
Trainable params: 41,678
Non-trainable params: 0
The following code creates the zero-valued vectors you will use to initialize x
and the LSTM state variables a
and c
.
x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))
Exercise: Implement predict_and_sample()
.
pred
should be a list of length $T_y$ where each element is a numpy-array of shape (1, n_values).inference_model.predict([input_x_init, hidden_state_init, cell_state_init])
predict
from the input arguments of this predict_and_sample
function.pred
into a numpy array of $T_y$ indices. argmax
of an element of the pred
list. axis
parameter.num_classes
parameter. Note that for grading purposes: you'll need to either:predict_and_sample()
(for example, one of the dimensions of x_initializer has the value for the number of distinct classes).# GRADED FUNCTION: predict_and_sample
def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer,
c_initializer = c_initializer):
"""
Predicts the next value of values using the inference model.
Arguments:
inference_model -- Keras model instance for inference time
x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation
a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell
c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel
Returns:
results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated
indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated
"""
### START CODE HERE ###
# Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.
pred = inference_model.predict([x_initializer, a_initializer, c_initializer])
# Step 2: Convert "pred" into an np.array() of indices with the maximum probabilities
indices = np.argmax(pred, axis = -1)
# Step 3: Convert indices to one-hot vectors, the shape of the results should be (Ty, n_values)
results = to_categorical(indices, num_classes = 78)
### END CODE HERE ###
return results, indices
results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
print("np.argmax(results[12]) =", np.argmax(results[12]))
print("np.argmax(results[17]) =", np.argmax(results[17]))
print("list(indices[12:18]) =", list(indices[12:18]))
np.argmax(results[12]) = 5 np.argmax(results[17]) = 21 list(indices[12:18]) = [array([5]), array([74]), array([8]), array([9]), array([50]), array([21])]
Expected (Approximate) Output:
**np.argmax(results[12])** = | 1 |
**np.argmax(results[17])** = | 42 |
**list(indices[12:18])** = | [array([1]), array([42]), array([54]), array([17]), array([1]), array([42])] |
Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your predict_and_sample()
function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time).
Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot of the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so let's use it in our implementation as well.
Let's make some music!
Run the following cell to generate music and record it into your out_stream
. This can take a couple of minutes.
out_stream = generate_music(inference_model)
Predicting new values for different set of chords. Generated 51 sounds using the predicted values for the set of chords ("1") and after pruning Generated 50 sounds using the predicted values for the set of chords ("2") and after pruning Generated 51 sounds using the predicted values for the set of chords ("3") and after pruning Generated 50 sounds using the predicted values for the set of chords ("4") and after pruning Generated 51 sounds using the predicted values for the set of chords ("5") and after pruning Your generated music is saved in output/my_music.midi
To listen to your music, click File->Open... Then go to "output/" and download "my_music.midi". Either play it on your computer with an application that can read midi files if you have one, or use one of the free online "MIDI to mp3" conversion tools to convert this to mp3.
As a reference, here is a 30 second audio clip we generated using this algorithm.
IPython.display.Audio('./data/30s_trained_model.mp3')
You have come to the end of the notebook.
Congratulations on completing this assignment and generating a jazz solo!
References
The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim's GitHub repository.
We're also grateful to François Germain for valuable feedback.