int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1 Default GPU Device: /gpu:0
Implement the get_inputs()
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
name
parameter.Return the placeholders in the following the tuple (Input, Targets, LearingRate)
def get_inputs():
"""
Create TF Placeholders for input, targets, and learning rate.
:return: Tuple (input, targets, learning rate)
"""
# TODO: Implement Function
input_text = tf.placeholder(tf.int32, [None, None], name='input')
target = tf.placeholder(tf.int32, [None, None], name='targets')
learning_rate = tf.placeholder(tf.float32, name='learningRate')
return input_text, target, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_inputs(get_inputs)
Tests Passed
Stack one or more BasicLSTMCells
in a MultiRNNCell
.
rnn_size
zero_state()
functiontf.identity()
Return the cell and initial state in the following tuple (Cell, InitialState)
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
# TODO: Implement Function
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
cell = tf.contrib.rnn.MultiRNNCell([lstm] * 3)
initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name='initial_state')
return cell, initial_state
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_init_cell(get_init_cell)
Tests Passed
Apply embedding to input_data
using TensorFlow. Return the embedded sequence.
def get_embed(input_data, vocab_size, embed_dim):
"""
Create embedding for <input_data>.
:param input_data: TF placeholder for text input.
:param vocab_size: Number of words in vocabulary.
:param embed_dim: Number of embedding dimensions
:return: Embedded input.
 Time to use the cell to create a RNN.
tf.nn.dynamic_rnn()
tf.identity()
Return the outputs and final_state state in the following tuple (Outputs, FinalState)
def build_rnn(cell, inputs):
"""
Create a RNN using a RNN Cell
:param cell: RNN Cell
:param inputs: Input text data
:return: Tuple (Outputs, Final State)
"""
# TODO: Implement Function
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype='float32')
final_state = tf.identity(final_state, name='final_state')
return outputs, final_state
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_rnn(build_rnn)
Tests Passed
Apply the functions you implemented above to:
input_data
using your get_embed(input_data, vocab_size, embed_dim)
function.cell
and your build_rnn(cell, inputs)
function.vocab_size
as the number of outputs.Return the logits and final state in the following tuple (Logits, FinalState)
def build_nn(cell, rnn_size, input_data, vocab_size):
"""
Build part of the neural network
:param cell: RNN cell
:param rnn_size: Size of rnns
:param input_data: Input data
:param vocab_size: Vocabulary size
:return: Tuple (Logits, FinalState)
"""
# TODO: Implement Function
embed = get_embed(input_data, vocab_size, 300)
outputs, final_state = build_rnn(cell, embed)
logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)
return logits, final_state
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_nn(build_nn)
Tests Passed
Implement get_batches
to create batches of input and targets using int_text
. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length)
. Each batch contains two elements:
[batch size, sequence length]
[batch size, sequence length]
If you can't fill the last batch with enough data, drop the last batch.
For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)
would return a Numpy array of the following:
[
# First Batch
[
# Batch of Input
[[ 1 2 3], [ 7 8 9]],
# Batch of targets
[[ 2 3 4], [ 8 9 10]]
],
# Second Batch
[
# Batch of Input
[[ 4 5 6], [10 11 12]],
# Batch of targets
[[ 5 6 7], [11 12 13]]
]
]
def get_batches(int_text, batch_size, seq_length):
"""
Return batches of input and target
:param int_text: Text with the words replaced by their ids
:param batch_size: The size of batch
:param seq_length: The length of sequence
:return: Batches as a Numpy array
"""
# TODO: Implement Function
x = []
y = []
for i in range(0, len(int_text) - seq_length, seq_length):
x.append(int_text[i: i + seq_length])
y.append(int_text[i+1: i + 1+ seq_length])
n_batches = len(x)//batch_size
x, y = x[:n_batches*batch_size], y[:n_batches*batch_size]
batches = np.zeros((n_batches, 2, batch_size, seq_length))
for ii in range(0, n_batches):
for iii in range(0, len(x), batch_size):
batches[ii][0] = x[iii:iii+batch_size]
batches[ii][1] = y[iii:iii+batch_size]
return batches
'''
n_batches = len(int_text) // (batch_size * seq_length)
x = np.array(int_text[:n_batches * batch_size * seq_length])
y = np.array(int_text[1:n_batches * batch_size * seq_length + 1])
x_batches = np.split(x.reshape(batch_size, -1), n_batches, 1)
y_batches = np.split(y.reshape(batch_size, -1), n_batches, 1)
batches = np.array(list(zip(y_batches, x_batches)))
return batches
'''
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_batches(get_batches)
Tests Passed
# a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
# a.reshape(2, -1)
# x = np.split(a.reshape(2, -1), 2, 1)
# x
# b = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
# b.reshape(2, -1)
# y = np.split(b.reshape(2, -1), 2, 1)
# y
# ba = np.array(list(zip(x, y)))
# ba
Tune the following parameters:
num_epochs
to the number of epochs.batch_size
to the batch size.rnn_size
to the size of the RNNs.seq_length
to the length of sequence.learning_rate
to the learning rate.show_every_n_batches
to the number of batches the neural network should print progress.# Number of Epochs
num_epochs = 128
# Batch Size
batch_size = 256
# RNN Size
rnn_size = 512
# Sequence Length
seq_length = 16
# Learning Rate
learning_rate = 0.01
# Show stats for every n number of batches
show_every_n_batches = 1
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
save_dir = './save'
Build the graph using the neural network you implemented.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from tensorflow.contrib import seq2seq
train_graph = tf.Graph()
with train_graph.as_default():
vocab_size = len(int_to_vocab)
input_text, targets, lr = get_inputs()
input_data_shape = tf.shape(input_text)
cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)
# Probabilities for generating words
probs = tf.nn.softmax(logits, name='probs')
# Loss function
cost = seq2seq.sequence_loss(
logits,
targets,
tf.ones([input_data_shape[0], input_data_shape[1]]))
# Optimizer
optimizer = tf.train.AdamOptimizer(lr)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients]
train_op = optimizer.apply_gradients(capped_gradients)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
batches = get_batches(int_text, batch_size, seq_length)
with tf.Session(graph=train_graph) as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(num_epochs):
state = sess.run(initial_state, {input_text: batches[0][0]})
for batch_i, (x, y) in enumerate(batches):
feed = {
input_text: x,
targets: y,
initial_state: state,
lr: learning_rate}
train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
# Show every <show_every_n_batches> batches
if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(
epoch_i,
batch_i,
len(batches),
train_loss))
# Save Model
saver = tf.train.Saver()
saver.save(sess, save_dir)
print('Model Trained and Saved')
Epoch 0 Batch 0/16 train_loss = 8.822 Epoch 0 Batch 1/16 train_loss = 8.793 Epoch 0 Batch 2/16 train_loss = 6.697 Epoch 0 Batch 3/16 train_loss = 6.182 Epoch 0 Batch 4/16 train_loss = 6.232 Epoch 0 Batch 5/16 train_loss = 5.851 Epoch 0 Batch 6/16 train_loss = 5.926 Epoch 0 Batch 7/16 train_loss = 5.877 Epoch 0 Batch 8/16 train_loss = 5.729 Epoch 0 Batch 9/16 train_loss = 5.710 Epoch 0 Batch 10/16 train_loss = 5.749 Epoch 0 Batch 11/16 train_loss = 5.739 Epoch 0 Batch 12/16 train_loss = 5.645 Epoch 0 Batch 13/16 train_loss = 5.579 Epoch 0 Batch 14/16 train_loss = 5.572 Epoch 0 Batch 15/16 train_loss = 5.575 Epoch 1 Batch 0/16 train_loss = 5.581 Epoch 1 Batch 1/16 train_loss = 5.538 Epoch 1 Batch 2/16 train_loss = 5.517 Epoch 1 Batch 3/16 train_loss = 5.509 Epoch 1 Batch 4/16 train_loss = 5.499 Epoch 1 Batch 5/16 train_loss = 5.480 Epoch 1 Batch 6/16 train_loss = 5.461 Epoch 1 Batch 7/16 train_loss = 5.448 Epoch 1 Batch 8/16 train_loss = 5.437 Epoch 1 Batch 9/16 train_loss = 5.423 Epoch 1 Batch 10/16 train_loss = 5.415 Epoch 1 Batch 11/16 train_loss = 5.407 Epoch 1 Batch 12/16 train_loss = 5.405 Epoch 1 Batch 13/16 train_loss = 5.399 Epoch 1 Batch 14/16 train_loss = 5.389 Epoch 1 Batch 15/16 train_loss = 5.381 Epoch 2 Batch 0/16 train_loss = 5.378 Epoch 2 Batch 1/16 train_loss = 5.373 Epoch 2 Batch 2/16 train_loss = 5.367 Epoch 2 Batch 3/16 train_loss = 5.362 Epoch 2 Batch 4/16 train_loss = 5.356 Epoch 2 Batch 5/16 train_loss = 5.352 Epoch 2 Batch 6/16 train_loss = 5.347 Epoch 2 Batch 7/16 train_loss = 5.342 Epoch 2 Batch 8/16 train_loss = 5.338 Epoch 2 Batch 9/16 train_loss = 5.333 Epoch 2 Batch 10/16 train_loss = 5.328 Epoch 2 Batch 11/16 train_loss = 5.322 Epoch 2 Batch 12/16 train_loss = 5.317 Epoch 2 Batch 13/16 train_loss = 5.313 Epoch 2 Batch 14/16 train_loss = 5.309 Epoch 2 Batch 15/16 train_loss = 5.305 Epoch 3 Batch 0/16 train_loss = 5.299 Epoch 3 Batch 1/16 train_loss = 5.295 Epoch 3 Batch 2/16 train_loss = 5.290 Epoch 3 Batch 3/16 train_loss = 5.284 Epoch 3 Batch 4/16 train_loss = 5.280 Epoch 3 Batch 5/16 train_loss = 5.275 Epoch 3 Batch 6/16 train_loss = 5.270 Epoch 3 Batch 7/16 train_loss = 5.264 Epoch 3 Batch 8/16 train_loss = 5.258 Epoch 3 Batch 9/16 train_loss = 5.254 Epoch 3 Batch 10/16 train_loss = 5.252 Epoch 3 Batch 11/16 train_loss = 5.246 Epoch 3 Batch 12/16 train_loss = 5.250 Epoch 3 Batch 13/16 train_loss = 5.247 Epoch 3 Batch 14/16 train_loss = 5.242 Epoch 3 Batch 15/16 train_loss = 5.237 Epoch 4 Batch 0/16 train_loss = 5.235 Epoch 4 Batch 1/16 train_loss = 5.234 Epoch 4 Batch 2/16 train_loss = 5.250 Epoch 4 Batch 3/16 train_loss = 5.228 Epoch 4 Batch 4/16 train_loss = 5.222 Epoch 4 Batch 5/16 train_loss = 5.215 Epoch 4 Batch 6/16 train_loss = 5.210 Epoch 4 Batch 7/16 train_loss = 5.209 Epoch 4 Batch 8/16 train_loss = 5.210 Epoch 4 Batch 9/16 train_loss = 5.221 Epoch 4 Batch 10/16 train_loss = 5.216 Epoch 4 Batch 11/16 train_loss = 5.199 Epoch 4 Batch 12/16 train_loss = 5.196 Epoch 4 Batch 13/16 train_loss = 5.192 Epoch 4 Batch 14/16 train_loss = 5.190 Epoch 4 Batch 15/16 train_loss = 5.183 Epoch 5 Batch 0/16 train_loss = 5.179 Epoch 5 Batch 1/16 train_loss = 5.174 Epoch 5 Batch 2/16 train_loss = 5.168 Epoch 5 Batch 3/16 train_loss = 5.164 Epoch 5 Batch 4/16 train_loss = 5.162 Epoch 5 Batch 5/16 train_loss = 5.159 Epoch 5 Batch 6/16 train_loss = 5.163 Epoch 5 Batch 7/16 train_loss = 5.152 Epoch 5 Batch 8/16 train_loss = 5.156 Epoch 5 Batch 9/16 train_loss = 5.147 Epoch 5 Batch 10/16 train_loss = 5.140 Epoch 5 Batch 11/16 train_loss = 5.137 Epoch 5 Batch 12/16 train_loss = 5.133 Epoch 5 Batch 13/16 train_loss = 5.128 Epoch 5 Batch 14/16 train_loss = 5.121 Epoch 5 Batch 15/16 train_loss = 5.112 Epoch 6 Batch 0/16 train_loss = 5.110 Epoch 6 Batch 1/16 train_loss = 5.109 Epoch 6 Batch 2/16 train_loss = 5.103 Epoch 6 Batch 3/16 train_loss = 5.101 Epoch 6 Batch 4/16 train_loss = 5.093 Epoch 6 Batch 5/16 train_loss = 5.087 Epoch 6 Batch 6/16 train_loss = 5.084 Epoch 6 Batch 7/16 train_loss = 5.079 Epoch 6 Batch 8/16 train_loss = 5.076 Epoch 6 Batch 9/16 train_loss = 5.076 Epoch 6 Batch 10/16 train_loss = 5.065 Epoch 6 Batch 11/16 train_loss = 5.088 Epoch 6 Batch 12/16 train_loss = 5.095 Epoch 6 Batch 13/16 train_loss = 5.093 Epoch 6 Batch 14/16 train_loss = 5.080 Epoch 6 Batch 15/16 train_loss = 5.080 Epoch 7 Batch 0/16 train_loss = 5.076 Epoch 7 Batch 1/16 train_loss = 5.075 Epoch 7 Batch 2/16 train_loss = 5.065 Epoch 7 Batch 3/16 train_loss = 5.081 Epoch 7 Batch 4/16 train_loss = 5.118 Epoch 7 Batch 5/16 train_loss = 5.102 Epoch 7 Batch 6/16 train_loss = 5.096 Epoch 7 Batch 7/16 train_loss = 5.113 Epoch 7 Batch 8/16 train_loss = 5.080 Epoch 7 Batch 9/16 train_loss = 5.075 Epoch 7 Batch 10/16 train_loss = 5.067 Epoch 7 Batch 11/16 train_loss = 5.048 Epoch 7 Batch 12/16 train_loss = 5.050 Epoch 7 Batch 13/16 train_loss = 5.051 Epoch 7 Batch 14/16 train_loss = 5.042 Epoch 7 Batch 15/16 train_loss = 5.037 Epoch 8 Batch 0/16 train_loss = 5.026 Epoch 8 Batch 1/16 train_loss = 5.015 Epoch 8 Batch 2/16 train_loss = 5.006 Epoch 8 Batch 3/16 train_loss = 5.004 Epoch 8 Batch 4/16 train_loss = 4.996 Epoch 8 Batch 5/16 train_loss = 4.988 Epoch 8 Batch 6/16 train_loss = 4.981 Epoch 8 Batch 7/16 train_loss = 4.972 Epoch 8 Batch 8/16 train_loss = 4.963 Epoch 8 Batch 9/16 train_loss = 4.954 Epoch 8 Batch 10/16 train_loss = 4.954 Epoch 8 Batch 11/16 train_loss = 4.964 Epoch 8 Batch 12/16 train_loss = 4.948 Epoch 8 Batch 13/16 train_loss = 4.939 Epoch 8 Batch 14/16 train_loss = 4.940 Epoch 8 Batch 15/16 train_loss = 4.929 Epoch 9 Batch 0/16 train_loss = 4.924 Epoch 9 Batch 1/16 train_loss = 4.916 Epoch 9 Batch 2/16 train_loss = 4.910 Epoch 9 Batch 3/16 train_loss = 4.904 Epoch 9 Batch 4/16 train_loss = 4.895 Epoch 9 Batch 5/16 train_loss = 4.893 Epoch 9 Batch 6/16 train_loss = 4.884 Epoch 9 Batch 7/16 train_loss = 4.878 Epoch 9 Batch 8/16 train_loss = 4.871 Epoch 9 Batch 9/16 train_loss = 4.865 Epoch 9 Batch 10/16 train_loss = 4.857 Epoch 9 Batch 11/16 train_loss = 4.850 Epoch 9 Batch 12/16 train_loss = 4.844 Epoch 9 Batch 13/16 train_loss = 4.837 Epoch 9 Batch 14/16 train_loss = 4.831 Epoch 9 Batch 15/16 train_loss = 4.824 Epoch 10 Batch 0/16 train_loss = 4.827 Epoch 10 Batch 1/16 train_loss = 4.822 Epoch 10 Batch 2/16 train_loss = 4.820 Epoch 10 Batch 3/16 train_loss = 4.811 Epoch 10 Batch 4/16 train_loss = 4.838 Epoch 10 Batch 5/16 train_loss = 4.833 Epoch 10 Batch 6/16 train_loss = 4.848 Epoch 10 Batch 7/16 train_loss = 4.848 Epoch 10 Batch 8/16 train_loss = 4.870 Epoch 10 Batch 9/16 train_loss = 4.830 Epoch 10 Batch 10/16 train_loss = 4.810 Epoch 10 Batch 11/16 train_loss = 4.800 Epoch 10 Batch 12/16 train_loss = 4.801 Epoch 10 Batch 13/16 train_loss = 4.848 Epoch 10 Batch 14/16 train_loss = 4.899 Epoch 10 Batch 15/16 train_loss = 4.860 Epoch 11 Batch 0/16 train_loss = 4.851 Epoch 11 Batch 1/16 train_loss = 4.870 Epoch 11 Batch 2/16 train_loss = 4.831 Epoch 11 Batch 3/16 train_loss = 4.822 Epoch 11 Batch 4/16 train_loss = 4.806 Epoch 11 Batch 5/16 train_loss = 4.770 Epoch 11 Batch 6/16 train_loss = 4.795 Epoch 11 Batch 7/16 train_loss = 4.755 Epoch 11 Batch 8/16 train_loss = 4.745 Epoch 11 Batch 9/16 train_loss = 4.735 Epoch 11 Batch 10/16 train_loss = 4.722 Epoch 11 Batch 11/16 train_loss = 4.705 Epoch 11 Batch 12/16 train_loss = 4.690 Epoch 11 Batch 13/16 train_loss = 4.679 Epoch 11 Batch 14/16 train_loss = 4.659 Epoch 11 Batch 15/16 train_loss = 4.645 Epoch 12 Batch 0/16 train_loss = 4.637 Epoch 12 Batch 1/16 train_loss = 4.639 Epoch 12 Batch 2/16 train_loss = 4.618 Epoch 12 Batch 3/16 train_loss = 4.597 Epoch 12 Batch 4/16 train_loss = 4.575 Epoch 12 Batch 5/16 train_loss = 4.564 Epoch 12 Batch 6/16 train_loss = 4.554 Epoch 12 Batch 7/16 train_loss = 4.535 Epoch 12 Batch 8/16 train_loss = 4.525 Epoch 12 Batch 9/16 train_loss = 4.519 Epoch 12 Batch 10/16 train_loss = 4.514 Epoch 12 Batch 11/16 train_loss = 4.520 Epoch 12 Batch 12/16 train_loss = 4.492 Epoch 12 Batch 13/16 train_loss = 4.482 Epoch 12 Batch 14/16 train_loss = 4.466 Epoch 12 Batch 15/16 train_loss = 4.449 Epoch 13 Batch 0/16 train_loss = 4.436 Epoch 13 Batch 1/16 train_loss = 4.421 Epoch 13 Batch 2/16 train_loss = 4.417 Epoch 13 Batch 3/16 train_loss = 4.407 Epoch 13 Batch 4/16 train_loss = 4.381 Epoch 13 Batch 5/16 train_loss = 4.367 Epoch 13 Batch 6/16 train_loss = 4.353 Epoch 13 Batch 7/16 train_loss = 4.339 Epoch 13 Batch 8/16 train_loss = 4.324 Epoch 13 Batch 9/16 train_loss = 4.316 Epoch 13 Batch 10/16 train_loss = 4.316 Epoch 13 Batch 11/16 train_loss = 4.289 Epoch 13 Batch 12/16 train_loss = 4.268 Epoch 13 Batch 13/16 train_loss = 4.251 Epoch 13 Batch 14/16 train_loss = 4.239 Epoch 13 Batch 15/16 train_loss = 4.232 Epoch 14 Batch 0/16 train_loss = 4.234 Epoch 14 Batch 1/16 train_loss = 4.258 Epoch 14 Batch 2/16 train_loss = 4.196 Epoch 14 Batch 3/16 train_loss = 4.223 Epoch 14 Batch 4/16 train_loss = 4.225 Epoch 14 Batch 5/16 train_loss = 4.186 Epoch 14 Batch 6/16 train_loss = 4.215 Epoch 14 Batch 7/16 train_loss = 4.184 Epoch 14 Batch 8/16 train_loss = 4.167 Epoch 14 Batch 9/16 train_loss = 4.149 Epoch 14 Batch 10/16 train_loss = 4.118 Epoch 14 Batch 11/16 train_loss = 4.115 Epoch 14 Batch 12/16 train_loss = 4.093 Epoch 14 Batch 13/16 train_loss = 4.078 Epoch 14 Batch 14/16 train_loss = 4.065 Epoch 14 Batch 15/16 train_loss = 4.036 Epoch 15 Batch 0/16 train_loss = 4.044 Epoch 15 Batch 1/16 train_loss = 4.026 Epoch 15 Batch 2/16 train_loss = 3.998 Epoch 15 Batch 3/16 train_loss = 3.988 Epoch 15 Batch 4/16 train_loss = 3.975 Epoch 15 Batch 5/16 train_loss = 3.951 Epoch 15 Batch 6/16 train_loss = 3.953 Epoch 15 Batch 7/16 train_loss = 3.935 Epoch 15 Batch 8/16 train_loss = 3.915 Epoch 15 Batch 9/16 train_loss = 3.910 Epoch 15 Batch 10/16 train_loss = 3.903 Epoch 15 Batch 11/16 train_loss = 3.877 Epoch 15 Batch 12/16 train_loss = 3.865 Epoch 15 Batch 13/16 train_loss = 3.858 Epoch 15 Batch 14/16 train_loss = 3.840 Epoch 15 Batch 15/16 train_loss = 3.828 Epoch 16 Batch 0/16 train_loss = 3.810 Epoch 16 Batch 1/16 train_loss = 3.803 Epoch 16 Batch 2/16 train_loss = 3.808 Epoch 16 Batch 3/16 train_loss = 3.795 Epoch 16 Batch 4/16 train_loss = 3.795 Epoch 16 Batch 5/16 train_loss = 3.751 Epoch 16 Batch 6/16 train_loss = 3.757 Epoch 16 Batch 7/16 train_loss = 3.752 Epoch 16 Batch 8/16 train_loss = 3.754 Epoch 16 Batch 9/16 train_loss = 3.716 Epoch 16 Batch 10/16 train_loss = 3.717 Epoch 16 Batch 11/16 train_loss = 3.752 Epoch 16 Batch 12/16 train_loss = 3.728 Epoch 16 Batch 13/16 train_loss = 3.716 Epoch 16 Batch 14/16 train_loss = 3.689 Epoch 16 Batch 15/16 train_loss = 3.667 Epoch 17 Batch 0/16 train_loss = 3.651 Epoch 17 Batch 1/16 train_loss = 3.629 Epoch 17 Batch 2/16 train_loss = 3.616 Epoch 17 Batch 3/16 train_loss = 3.593 Epoch 17 Batch 4/16 train_loss = 3.575 Epoch 17 Batch 5/16 train_loss = 3.565 Epoch 17 Batch 6/16 train_loss = 3.548 Epoch 17 Batch 7/16 train_loss = 3.552 Epoch 17 Batch 8/16 train_loss = 3.544 Epoch 17 Batch 9/16 train_loss = 3.555 Epoch 17 Batch 10/16 train_loss = 3.557 Epoch 17 Batch 11/16 train_loss = 3.659 Epoch 17 Batch 12/16 train_loss = 3.630 Epoch 17 Batch 13/16 train_loss = 3.582 Epoch 17 Batch 14/16 train_loss = 3.660 Epoch 17 Batch 15/16 train_loss = 3.539 Epoch 18 Batch 0/16 train_loss = 3.551 Epoch 18 Batch 1/16 train_loss = 3.535 Epoch 18 Batch 2/16 train_loss = 3.488 Epoch 18 Batch 3/16 train_loss = 3.491 Epoch 18 Batch 4/16 train_loss = 3.441 Epoch 18 Batch 5/16 train_loss = 3.405 Epoch 18 Batch 6/16 train_loss = 3.399 Epoch 18 Batch 7/16 train_loss = 3.372 Epoch 18 Batch 8/16 train_loss = 3.354 Epoch 18 Batch 9/16 train_loss = 3.335 Epoch 18 Batch 10/16 train_loss = 3.310 Epoch 18 Batch 11/16 train_loss = 3.295 Epoch 18 Batch 12/16 train_loss = 3.283 Epoch 18 Batch 13/16 train_loss = 3.282 Epoch 18 Batch 14/16 train_loss = 3.302 Epoch 18 Batch 15/16 train_loss = 3.260 Epoch 19 Batch 0/16 train_loss = 3.222 Epoch 19 Batch 1/16 train_loss = 3.206 Epoch 19 Batch 2/16 train_loss = 3.196 Epoch 19 Batch 3/16 train_loss = 3.182 Epoch 19 Batch 4/16 train_loss = 3.155 Epoch 19 Batch 5/16 train_loss = 3.144 Epoch 19 Batch 6/16 train_loss = 3.117 Epoch 19 Batch 7/16 train_loss = 3.105 Epoch 19 Batch 8/16 train_loss = 3.099 Epoch 19 Batch 9/16 train_loss = 3.087 Epoch 19 Batch 10/16 train_loss = 3.067 Epoch 19 Batch 11/16 train_loss = 3.046 Epoch 19 Batch 12/16 train_loss = 3.038 Epoch 19 Batch 13/16 train_loss = 3.020 Epoch 19 Batch 14/16 train_loss = 3.002 Epoch 19 Batch 15/16 train_loss = 2.995 Epoch 20 Batch 0/16 train_loss = 2.979 Epoch 20 Batch 1/16 train_loss = 2.965 Epoch 20 Batch 2/16 train_loss = 2.965 Epoch 20 Batch 3/16 train_loss = 2.948 Epoch 20 Batch 4/16 train_loss = 2.936 Epoch 20 Batch 5/16 train_loss = 2.945 Epoch 20 Batch 6/16 train_loss = 2.955 Epoch 20 Batch 7/16 train_loss = 3.043 Epoch 20 Batch 8/16 train_loss = 2.998 Epoch 20 Batch 9/16 train_loss = 2.977 Epoch 20 Batch 10/16 train_loss = 2.919 Epoch 20 Batch 11/16 train_loss = 2.949 Epoch 20 Batch 12/16 train_loss = 2.899 Epoch 20 Batch 13/16 train_loss = 2.884 Epoch 20 Batch 14/16 train_loss = 2.890 Epoch 20 Batch 15/16 train_loss = 2.840 Epoch 21 Batch 0/16 train_loss = 2.844 Epoch 21 Batch 1/16 train_loss = 2.815 Epoch 21 Batch 2/16 train_loss = 2.814 Epoch 21 Batch 3/16 train_loss = 2.790 Epoch 21 Batch 4/16 train_loss = 2.784 Epoch 21 Batch 5/16 train_loss = 2.765 Epoch 21 Batch 6/16 train_loss = 2.750 Epoch 21 Batch 7/16 train_loss = 2.739 Epoch 21 Batch 8/16 train_loss = 2.726 Epoch 21 Batch 9/16 train_loss = 2.715 Epoch 21 Batch 10/16 train_loss = 2.700 Epoch 21 Batch 11/16 train_loss = 2.690 Epoch 21 Batch 12/16 train_loss = 2.679 Epoch 21 Batch 13/16 train_loss = 2.666 Epoch 21 Batch 14/16 train_loss = 2.656 Epoch 21 Batch 15/16 train_loss = 2.642 Epoch 22 Batch 0/16 train_loss = 2.633 Epoch 22 Batch 1/16 train_loss = 2.623 Epoch 22 Batch 2/16 train_loss = 2.611 Epoch 22 Batch 3/16 train_loss = 2.601 Epoch 22 Batch 4/16 train_loss = 2.591 Epoch 22 Batch 5/16 train_loss = 2.578 Epoch 22 Batch 6/16 train_loss = 2.568 Epoch 22 Batch 7/16 train_loss = 2.559 Epoch 22 Batch 8/16 train_loss = 2.548 Epoch 22 Batch 9/16 train_loss = 2.536 Epoch 22 Batch 10/16 train_loss = 2.526 Epoch 22 Batch 11/16 train_loss = 2.523 Epoch 22 Batch 12/16 train_loss = 2.529 Epoch 22 Batch 13/16 train_loss = 2.595 Epoch 22 Batch 14/16 train_loss = 2.594 Epoch 22 Batch 15/16 train_loss = 2.631 Epoch 23 Batch 0/16 train_loss = 2.516 Epoch 23 Batch 1/16 train_loss = 2.592 Epoch 23 Batch 2/16 train_loss = 2.528 Epoch 23 Batch 3/16 train_loss = 2.547 Epoch 23 Batch 4/16 train_loss = 2.565 Epoch 23 Batch 5/16 train_loss = 2.523 Epoch 23 Batch 6/16 train_loss = 2.532 Epoch 23 Batch 7/16 train_loss = 2.511 Epoch 23 Batch 8/16 train_loss = 2.483 Epoch 23 Batch 9/16 train_loss = 2.504 Epoch 23 Batch 10/16 train_loss = 2.465 Epoch 23 Batch 11/16 train_loss = 2.487 Epoch 23 Batch 12/16 train_loss = 2.435 Epoch 23 Batch 13/16 train_loss = 2.436 Epoch 23 Batch 14/16 train_loss = 2.421 Epoch 23 Batch 15/16 train_loss = 2.399 Epoch 24 Batch 0/16 train_loss = 2.384 Epoch 24 Batch 1/16 train_loss = 2.371 Epoch 24 Batch 2/16 train_loss = 2.361 Epoch 24 Batch 3/16 train_loss = 2.343 Epoch 24 Batch 4/16 train_loss = 2.331 Epoch 24 Batch 5/16 train_loss = 2.313 Epoch 24 Batch 6/16 train_loss = 2.305 Epoch 24 Batch 7/16 train_loss = 2.289 Epoch 24 Batch 8/16 train_loss = 2.280 Epoch 24 Batch 9/16 train_loss = 2.264 Epoch 24 Batch 10/16 train_loss = 2.254 Epoch 24 Batch 11/16 train_loss = 2.243 Epoch 24 Batch 12/16 train_loss = 2.241 Epoch 24 Batch 13/16 train_loss = 2.251 Epoch 24 Batch 14/16 train_loss = 2.231 Epoch 24 Batch 15/16 train_loss = 2.219 Epoch 25 Batch 0/16 train_loss = 2.216 Epoch 25 Batch 1/16 train_loss = 2.195 Epoch 25 Batch 2/16 train_loss = 2.190 Epoch 25 Batch 3/16 train_loss = 2.175 Epoch 25 Batch 4/16 train_loss = 2.163 Epoch 25 Batch 5/16 train_loss = 2.156 Epoch 25 Batch 6/16 train_loss = 2.135 Epoch 25 Batch 7/16 train_loss = 2.128 Epoch 25 Batch 8/16 train_loss = 2.133 Epoch 25 Batch 9/16 train_loss = 2.118 Epoch 25 Batch 10/16 train_loss = 2.119 Epoch 25 Batch 11/16 train_loss = 2.088 Epoch 25 Batch 12/16 train_loss = 2.085 Epoch 25 Batch 13/16 train_loss = 2.078 Epoch 25 Batch 14/16 train_loss = 2.067 Epoch 25 Batch 15/16 train_loss = 2.055 Epoch 26 Batch 0/16 train_loss = 2.047 Epoch 26 Batch 1/16 train_loss = 2.028 Epoch 26 Batch 2/16 train_loss = 2.017 Epoch 26 Batch 3/16 train_loss = 1.996 Epoch 26 Batch 4/16 train_loss = 1.995 Epoch 26 Batch 5/16 train_loss = 1.984 Epoch 26 Batch 6/16 train_loss = 1.990 Epoch 26 Batch 7/16 train_loss = 1.979 Epoch 26 Batch 8/16 train_loss = 1.977 Epoch 26 Batch 9/16 train_loss = 1.960 Epoch 26 Batch 10/16 train_loss = 1.966 Epoch 26 Batch 11/16 train_loss = 1.951 Epoch 26 Batch 12/16 train_loss = 1.939 Epoch 26 Batch 13/16 train_loss = 1.945 Epoch 26 Batch 14/16 train_loss = 1.915 Epoch 26 Batch 15/16 train_loss = 1.902 Epoch 27 Batch 0/16 train_loss = 1.898 Epoch 27 Batch 1/16 train_loss = 1.897 Epoch 27 Batch 2/16 train_loss = 1.859 Epoch 27 Batch 3/16 train_loss = 1.863 Epoch 27 Batch 4/16 train_loss = 1.837 Epoch 27 Batch 5/16 train_loss = 1.824 Epoch 27 Batch 6/16 train_loss = 1.816 Epoch 27 Batch 7/16 train_loss = 1.803 Epoch 27 Batch 8/16 train_loss = 1.790 Epoch 27 Batch 9/16 train_loss = 1.786 Epoch 27 Batch 10/16 train_loss = 1.806 Epoch 27 Batch 11/16 train_loss = 1.797 Epoch 27 Batch 12/16 train_loss = 1.798 Epoch 27 Batch 13/16 train_loss = 1.785 Epoch 27 Batch 14/16 train_loss = 1.796 Epoch 27 Batch 15/16 train_loss = 1.754 Epoch 28 Batch 0/16 train_loss = 1.768 Epoch 28 Batch 1/16 train_loss = 1.756 Epoch 28 Batch 2/16 train_loss = 1.748 Epoch 28 Batch 3/16 train_loss = 1.743 Epoch 28 Batch 4/16 train_loss = 1.728 Epoch 28 Batch 5/16 train_loss = 1.736 Epoch 28 Batch 6/16 train_loss = 1.706 Epoch 28 Batch 7/16 train_loss = 1.704 Epoch 28 Batch 8/16 train_loss = 1.691 Epoch 28 Batch 9/16 train_loss = 1.683 Epoch 28 Batch 10/16 train_loss = 1.691 Epoch 28 Batch 11/16 train_loss = 1.683 Epoch 28 Batch 12/16 train_loss = 1.707 Epoch 28 Batch 13/16 train_loss = 1.658 Epoch 28 Batch 14/16 train_loss = 1.650 Epoch 28 Batch 15/16 train_loss = 1.636 Epoch 29 Batch 0/16 train_loss = 1.619 Epoch 29 Batch 1/16 train_loss = 1.597 Epoch 29 Batch 2/16 train_loss = 1.594 Epoch 29 Batch 3/16 train_loss = 1.572 Epoch 29 Batch 4/16 train_loss = 1.567 Epoch 29 Batch 5/16 train_loss = 1.550 Epoch 29 Batch 6/16 train_loss = 1.560 Epoch 29 Batch 7/16 train_loss = 1.557 Epoch 29 Batch 8/16 train_loss = 1.537 Epoch 29 Batch 9/16 train_loss = 1.527 Epoch 29 Batch 10/16 train_loss = 1.514 Epoch 29 Batch 11/16 train_loss = 1.500 Epoch 29 Batch 12/16 train_loss = 1.491 Epoch 29 Batch 13/16 train_loss = 1.497 Epoch 29 Batch 14/16 train_loss = 1.525 Epoch 29 Batch 15/16 train_loss = 1.581 Epoch 30 Batch 0/16 train_loss = 1.693 Epoch 30 Batch 1/16 train_loss = 1.533 Epoch 30 Batch 2/16 train_loss = 1.601 Epoch 30 Batch 3/16 train_loss = 1.696 Epoch 30 Batch 4/16 train_loss = 1.601 Epoch 30 Batch 5/16 train_loss = 1.604 Epoch 30 Batch 6/16 train_loss = 1.517 Epoch 30 Batch 7/16 train_loss = 1.550 Epoch 30 Batch 8/16 train_loss = 1.595 Epoch 30 Batch 9/16 train_loss = 1.596 Epoch 30 Batch 10/16 train_loss = 1.568 Epoch 30 Batch 11/16 train_loss = 1.495 Epoch 30 Batch 12/16 train_loss = 1.484 Epoch 30 Batch 13/16 train_loss = 1.499 Epoch 30 Batch 14/16 train_loss = 1.442 Epoch 30 Batch 15/16 train_loss = 1.430 Epoch 31 Batch 0/16 train_loss = 1.424 Epoch 31 Batch 1/16 train_loss = 1.398 Epoch 31 Batch 2/16 train_loss = 1.394 Epoch 31 Batch 3/16 train_loss = 1.363 Epoch 31 Batch 4/16 train_loss = 1.356 Epoch 31 Batch 5/16 train_loss = 1.336 Epoch 31 Batch 6/16 train_loss = 1.329 Epoch 31 Batch 7/16 train_loss = 1.304 Epoch 31 Batch 8/16 train_loss = 1.300 Epoch 31 Batch 9/16 train_loss = 1.285 Epoch 31 Batch 10/16 train_loss = 1.268 Epoch 31 Batch 11/16 train_loss = 1.257 Epoch 31 Batch 12/16 train_loss = 1.249 Epoch 31 Batch 13/16 train_loss = 1.237 Epoch 31 Batch 14/16 train_loss = 1.224 Epoch 31 Batch 15/16 train_loss = 1.215 Epoch 32 Batch 0/16 train_loss = 1.204 Epoch 32 Batch 1/16 train_loss = 1.193 Epoch 32 Batch 2/16 train_loss = 1.183 Epoch 32 Batch 3/16 train_loss = 1.172 Epoch 32 Batch 4/16 train_loss = 1.164 Epoch 32 Batch 5/16 train_loss = 1.154 Epoch 32 Batch 6/16 train_loss = 1.144 Epoch 32 Batch 7/16 train_loss = 1.135 Epoch 32 Batch 8/16 train_loss = 1.126 Epoch 32 Batch 9/16 train_loss = 1.117 Epoch 32 Batch 10/16 train_loss = 1.108 Epoch 32 Batch 11/16 train_loss = 1.099 Epoch 32 Batch 12/16 train_loss = 1.089 Epoch 32 Batch 13/16 train_loss = 1.081 Epoch 32 Batch 14/16 train_loss = 1.074 Epoch 32 Batch 15/16 train_loss = 1.068 Epoch 33 Batch 0/16 train_loss = 1.063 Epoch 33 Batch 1/16 train_loss = 1.058 Epoch 33 Batch 2/16 train_loss = 1.056 Epoch 33 Batch 3/16 train_loss = 1.043 Epoch 33 Batch 4/16 train_loss = 1.028 Epoch 33 Batch 5/16 train_loss = 1.014 Epoch 33 Batch 6/16 train_loss = 1.013 Epoch 33 Batch 7/16 train_loss = 1.015 Epoch 33 Batch 8/16 train_loss = 1.006 Epoch 33 Batch 9/16 train_loss = 0.995 Epoch 33 Batch 10/16 train_loss = 0.987 Epoch 33 Batch 11/16 train_loss = 0.978 Epoch 33 Batch 12/16 train_loss = 0.973 Epoch 33 Batch 13/16 train_loss = 0.964 Epoch 33 Batch 14/16 train_loss = 0.960 Epoch 33 Batch 15/16 train_loss = 0.954 Epoch 34 Batch 0/16 train_loss = 0.936 Epoch 34 Batch 1/16 train_loss = 0.930 Epoch 34 Batch 2/16 train_loss = 0.936 Epoch 34 Batch 3/16 train_loss = 0.929 Epoch 34 Batch 4/16 train_loss = 0.913 Epoch 34 Batch 5/16 train_loss = 0.904 Epoch 34 Batch 6/16 train_loss = 0.898 Epoch 34 Batch 7/16 train_loss = 0.893 Epoch 34 Batch 8/16 train_loss = 0.882 Epoch 34 Batch 9/16 train_loss = 0.879 Epoch 34 Batch 10/16 train_loss = 0.874 Epoch 34 Batch 11/16 train_loss = 0.863 Epoch 34 Batch 12/16 train_loss = 0.857 Epoch 34 Batch 13/16 train_loss = 0.842 Epoch 34 Batch 14/16 train_loss = 0.838 Epoch 34 Batch 15/16 train_loss = 0.834 Epoch 35 Batch 0/16 train_loss = 0.830 Epoch 35 Batch 1/16 train_loss = 0.836 Epoch 35 Batch 2/16 train_loss = 0.870 Epoch 35 Batch 3/16 train_loss = 0.857 Epoch 35 Batch 4/16 train_loss = 0.854 Epoch 35 Batch 5/16 train_loss = 0.829 Epoch 35 Batch 6/16 train_loss = 0.842 Epoch 35 Batch 7/16 train_loss = 0.818 Epoch 35 Batch 8/16 train_loss = 0.805 Epoch 35 Batch 9/16 train_loss = 0.791 Epoch 35 Batch 10/16 train_loss = 0.789 Epoch 35 Batch 11/16 train_loss = 0.774 Epoch 35 Batch 12/16 train_loss = 0.774 Epoch 35 Batch 13/16 train_loss = 0.760 Epoch 35 Batch 14/16 train_loss = 0.751 Epoch 35 Batch 15/16 train_loss = 0.742 Epoch 36 Batch 0/16 train_loss = 0.737 Epoch 36 Batch 1/16 train_loss = 0.728 Epoch 36 Batch 2/16 train_loss = 0.722 Epoch 36 Batch 3/16 train_loss = 0.713 Epoch 36 Batch 4/16 train_loss = 0.707 Epoch 36 Batch 5/16 train_loss = 0.700 Epoch 36 Batch 6/16 train_loss = 0.693 Epoch 36 Batch 7/16 train_loss = 0.687 Epoch 36 Batch 8/16 train_loss = 0.680 Epoch 36 Batch 9/16 train_loss = 0.676 Epoch 36 Batch 10/16 train_loss = 0.667 Epoch 36 Batch 11/16 train_loss = 0.663 Epoch 36 Batch 12/16 train_loss = 0.656 Epoch 36 Batch 13/16 train_loss = 0.651 Epoch 36 Batch 14/16 train_loss = 0.646 Epoch 36 Batch 15/16 train_loss = 0.641 Epoch 37 Batch 0/16 train_loss = 0.635 Epoch 37 Batch 1/16 train_loss = 0.630 Epoch 37 Batch 2/16 train_loss = 0.623 Epoch 37 Batch 3/16 train_loss = 0.618 Epoch 37 Batch 4/16 train_loss = 0.612 Epoch 37 Batch 5/16 train_loss = 0.607 Epoch 37 Batch 6/16 train_loss = 0.601 Epoch 37 Batch 7/16 train_loss = 0.596 Epoch 37 Batch 8/16 train_loss = 0.592 Epoch 37 Batch 9/16 train_loss = 0.588 Epoch 37 Batch 10/16 train_loss = 0.584 Epoch 37 Batch 11/16 train_loss = 0.580 Epoch 37 Batch 12/16 train_loss = 0.574 Epoch 37 Batch 13/16 train_loss = 0.569 Epoch 37 Batch 14/16 train_loss = 0.564 Epoch 37 Batch 15/16 train_loss = 0.561 Epoch 38 Batch 0/16 train_loss = 0.555 Epoch 38 Batch 1/16 train_loss = 0.550 Epoch 38 Batch 2/16 train_loss = 0.543 Epoch 38 Batch 3/16 train_loss = 0.540 Epoch 38 Batch 4/16 train_loss = 0.538 Epoch 38 Batch 5/16 train_loss = 0.537 Epoch 38 Batch 6/16 train_loss = 0.538 Epoch 38 Batch 7/16 train_loss = 0.535 Epoch 38 Batch 8/16 train_loss = 0.537 Epoch 38 Batch 9/16 train_loss = 0.554 Epoch 38 Batch 10/16 train_loss = 0.543 Epoch 38 Batch 11/16 train_loss = 0.530 Epoch 38 Batch 12/16 train_loss = 0.516 Epoch 38 Batch 13/16 train_loss = 0.520 Epoch 38 Batch 14/16 train_loss = 0.507 Epoch 38 Batch 15/16 train_loss = 0.507 Epoch 39 Batch 0/16 train_loss = 0.504 Epoch 39 Batch 1/16 train_loss = 0.495 Epoch 39 Batch 2/16 train_loss = 0.495 Epoch 39 Batch 3/16 train_loss = 0.485 Epoch 39 Batch 4/16 train_loss = 0.485 Epoch 39 Batch 5/16 train_loss = 0.480 Epoch 39 Batch 6/16 train_loss = 0.472 Epoch 39 Batch 7/16 train_loss = 0.470 Epoch 39 Batch 8/16 train_loss = 0.462 Epoch 39 Batch 9/16 train_loss = 0.460 Epoch 39 Batch 10/16 train_loss = 0.454 Epoch 39 Batch 11/16 train_loss = 0.450 Epoch 39 Batch 12/16 train_loss = 0.446 Epoch 39 Batch 13/16 train_loss = 0.442 Epoch 39 Batch 14/16 train_loss = 0.438 Epoch 39 Batch 15/16 train_loss = 0.434 Epoch 40 Batch 0/16 train_loss = 0.429 Epoch 40 Batch 1/16 train_loss = 0.426 Epoch 40 Batch 2/16 train_loss = 0.422 Epoch 40 Batch 3/16 train_loss = 0.419 Epoch 40 Batch 4/16 train_loss = 0.415 Epoch 40 Batch 5/16 train_loss = 0.411 Epoch 40 Batch 6/16 train_loss = 0.407 Epoch 40 Batch 7/16 train_loss = 0.404 Epoch 40 Batch 8/16 train_loss = 0.400 Epoch 40 Batch 9/16 train_loss = 0.397 Epoch 40 Batch 10/16 train_loss = 0.393 Epoch 40 Batch 11/16 train_loss = 0.390 Epoch 40 Batch 12/16 train_loss = 0.387 Epoch 40 Batch 13/16 train_loss = 0.384 Epoch 40 Batch 14/16 train_loss = 0.381 Epoch 40 Batch 15/16 train_loss = 0.377 Epoch 41 Batch 0/16 train_loss = 0.374 Epoch 41 Batch 1/16 train_loss = 0.371 Epoch 41 Batch 2/16 train_loss = 0.368 Epoch 41 Batch 3/16 train_loss = 0.365 Epoch 41 Batch 4/16 train_loss = 0.362 Epoch 41 Batch 5/16 train_loss = 0.358 Epoch 41 Batch 6/16 train_loss = 0.356 Epoch 41 Batch 7/16 train_loss = 0.353 Epoch 41 Batch 8/16 train_loss = 0.350 Epoch 41 Batch 9/16 train_loss = 0.348 Epoch 41 Batch 10/16 train_loss = 0.347 Epoch 41 Batch 11/16 train_loss = 0.345 Epoch 41 Batch 12/16 train_loss = 0.343 Epoch 41 Batch 13/16 train_loss = 0.338 Epoch 41 Batch 14/16 train_loss = 0.334 Epoch 41 Batch 15/16 train_loss = 0.335 Epoch 42 Batch 0/16 train_loss = 0.333 Epoch 42 Batch 1/16 train_loss = 0.329 Epoch 42 Batch 2/16 train_loss = 0.325 Epoch 42 Batch 3/16 train_loss = 0.323 Epoch 42 Batch 4/16 train_loss = 0.322 Epoch 42 Batch 5/16 train_loss = 0.320 Epoch 42 Batch 6/16 train_loss = 0.316 Epoch 42 Batch 7/16 train_loss = 0.317 Epoch 42 Batch 8/16 train_loss = 0.318 Epoch 42 Batch 9/16 train_loss = 0.322 Epoch 42 Batch 10/16 train_loss = 0.317 Epoch 42 Batch 11/16 train_loss = 0.315 Epoch 42 Batch 12/16 train_loss = 0.312 Epoch 42 Batch 13/16 train_loss = 0.308 Epoch 42 Batch 14/16 train_loss = 0.305 Epoch 42 Batch 15/16 train_loss = 0.303 Epoch 43 Batch 0/16 train_loss = 0.300 Epoch 43 Batch 1/16 train_loss = 0.297 Epoch 43 Batch 2/16 train_loss = 0.294 Epoch 43 Batch 3/16 train_loss = 0.292 Epoch 43 Batch 4/16 train_loss = 0.289 Epoch 43 Batch 5/16 train_loss = 0.287 Epoch 43 Batch 6/16 train_loss = 0.284 Epoch 43 Batch 7/16 train_loss = 0.282 Epoch 43 Batch 8/16 train_loss = 0.280 Epoch 43 Batch 9/16 train_loss = 0.279 Epoch 43 Batch 10/16 train_loss = 0.276 Epoch 43 Batch 11/16 train_loss = 0.274 Epoch 43 Batch 12/16 train_loss = 0.272 Epoch 43 Batch 13/16 train_loss = 0.270 Epoch 43 Batch 14/16 train_loss = 0.267 Epoch 43 Batch 15/16 train_loss = 0.265 Epoch 44 Batch 0/16 train_loss = 0.263 Epoch 44 Batch 1/16 train_loss = 0.262 Epoch 44 Batch 2/16 train_loss = 0.260 Epoch 44 Batch 3/16 train_loss = 0.260 Epoch 44 Batch 4/16 train_loss = 0.257 Epoch 44 Batch 5/16 train_loss = 0.254 Epoch 44 Batch 6/16 train_loss = 0.251 Epoch 44 Batch 7/16 train_loss = 0.249 Epoch 44 Batch 8/16 train_loss = 0.247 Epoch 44 Batch 9/16 train_loss = 0.245 Epoch 44 Batch 10/16 train_loss = 0.243 Epoch 44 Batch 11/16 train_loss = 0.241 Epoch 44 Batch 12/16 train_loss = 0.240 Epoch 44 Batch 13/16 train_loss = 0.238 Epoch 44 Batch 14/16 train_loss = 0.237 Epoch 44 Batch 15/16 train_loss = 0.235 Epoch 45 Batch 0/16 train_loss = 0.233 Epoch 45 Batch 1/16 train_loss = 0.232 Epoch 45 Batch 2/16 train_loss = 0.230 Epoch 45 Batch 3/16 train_loss = 0.228 Epoch 45 Batch 4/16 train_loss = 0.227 Epoch 45 Batch 5/16 train_loss = 0.224 Epoch 45 Batch 6/16 train_loss = 0.222 Epoch 45 Batch 7/16 train_loss = 0.222 Epoch 45 Batch 8/16 train_loss = 0.221 Epoch 45 Batch 9/16 train_loss = 0.220 Epoch 45 Batch 10/16 train_loss = 0.218 Epoch 45 Batch 11/16 train_loss = 0.217 Epoch 45 Batch 12/16 train_loss = 0.215 Epoch 45 Batch 13/16 train_loss = 0.214 Epoch 45 Batch 14/16 train_loss = 0.212 Epoch 45 Batch 15/16 train_loss = 0.210 Epoch 46 Batch 0/16 train_loss = 0.209 Epoch 46 Batch 1/16 train_loss = 0.207 Epoch 46 Batch 2/16 train_loss = 0.206 Epoch 46 Batch 3/16 train_loss = 0.205 Epoch 46 Batch 4/16 train_loss = 0.203 Epoch 46 Batch 5/16 train_loss = 0.201 Epoch 46 Batch 6/16 train_loss = 0.200 Epoch 46 Batch 7/16 train_loss = 0.199 Epoch 46 Batch 8/16 train_loss = 0.198 Epoch 46 Batch 9/16 train_loss = 0.196 Epoch 46 Batch 10/16 train_loss = 0.195 Epoch 46 Batch 11/16 train_loss = 0.193 Epoch 46 Batch 12/16 train_loss = 0.192 Epoch 46 Batch 13/16 train_loss = 0.191 Epoch 46 Batch 14/16 train_loss = 0.190 Epoch 46 Batch 15/16 train_loss = 0.190 Epoch 47 Batch 0/16 train_loss = 0.188 Epoch 47 Batch 1/16 train_loss = 0.189 Epoch 47 Batch 2/16 train_loss = 0.190 Epoch 47 Batch 3/16 train_loss = 0.190 Epoch 47 Batch 4/16 train_loss = 0.188 Epoch 47 Batch 5/16 train_loss = 0.187 Epoch 47 Batch 6/16 train_loss = 0.186 Epoch 47 Batch 7/16 train_loss = 0.184 Epoch 47 Batch 8/16 train_loss = 0.183 Epoch 47 Batch 9/16 train_loss = 0.181 Epoch 47 Batch 10/16 train_loss = 0.180 Epoch 47 Batch 11/16 train_loss = 0.179 Epoch 47 Batch 12/16 train_loss = 0.178 Epoch 47 Batch 13/16 train_loss = 0.177 Epoch 47 Batch 14/16 train_loss = 0.175 Epoch 47 Batch 15/16 train_loss = 0.174 Epoch 48 Batch 0/16 train_loss = 0.173 Epoch 48 Batch 1/16 train_loss = 0.172 Epoch 48 Batch 2/16 train_loss = 0.172 Epoch 48 Batch 3/16 train_loss = 0.170 Epoch 48 Batch 4/16 train_loss = 0.169 Epoch 48 Batch 5/16 train_loss = 0.168 Epoch 48 Batch 6/16 train_loss = 0.167 Epoch 48 Batch 7/16 train_loss = 0.167 Epoch 48 Batch 8/16 train_loss = 0.165 Epoch 48 Batch 9/16 train_loss = 0.165 Epoch 48 Batch 10/16 train_loss = 0.166 Epoch 48 Batch 11/16 train_loss = 0.166 Epoch 48 Batch 12/16 train_loss = 0.165 Epoch 48 Batch 13/16 train_loss = 0.163 Epoch 48 Batch 14/16 train_loss = 0.163 Epoch 48 Batch 15/16 train_loss = 0.161 Epoch 49 Batch 0/16 train_loss = 0.160 Epoch 49 Batch 1/16 train_loss = 0.159 Epoch 49 Batch 2/16 train_loss = 0.159 Epoch 49 Batch 3/16 train_loss = 0.157 Epoch 49 Batch 4/16 train_loss = 0.157 Epoch 49 Batch 5/16 train_loss = 0.155 Epoch 49 Batch 6/16 train_loss = 0.155 Epoch 49 Batch 7/16 train_loss = 0.154 Epoch 49 Batch 8/16 train_loss = 0.153 Epoch 49 Batch 9/16 train_loss = 0.152 Epoch 49 Batch 10/16 train_loss = 0.152 Epoch 49 Batch 11/16 train_loss = 0.151 Epoch 49 Batch 12/16 train_loss = 0.150 Epoch 49 Batch 13/16 train_loss = 0.149 Epoch 49 Batch 14/16 train_loss = 0.149 Epoch 49 Batch 15/16 train_loss = 0.148 Epoch 50 Batch 0/16 train_loss = 0.147 Epoch 50 Batch 1/16 train_loss = 0.146 Epoch 50 Batch 2/16 train_loss = 0.146 Epoch 50 Batch 3/16 train_loss = 0.145 Epoch 50 Batch 4/16 train_loss = 0.144 Epoch 50 Batch 5/16 train_loss = 0.144 Epoch 50 Batch 6/16 train_loss = 0.143 Epoch 50 Batch 7/16 train_loss = 0.142 Epoch 50 Batch 8/16 train_loss = 0.142 Epoch 50 Batch 9/16 train_loss = 0.141 Epoch 50 Batch 10/16 train_loss = 0.140 Epoch 50 Batch 11/16 train_loss = 0.140 Epoch 50 Batch 12/16 train_loss = 0.139 Epoch 50 Batch 13/16 train_loss = 0.139 Epoch 50 Batch 14/16 train_loss = 0.138 Epoch 50 Batch 15/16 train_loss = 0.137 Epoch 51 Batch 0/16 train_loss = 0.137 Epoch 51 Batch 1/16 train_loss = 0.136 Epoch 51 Batch 2/16 train_loss = 0.136 Epoch 51 Batch 3/16 train_loss = 0.135 Epoch 51 Batch 4/16 train_loss = 0.134 Epoch 51 Batch 5/16 train_loss = 0.134 Epoch 51 Batch 6/16 train_loss = 0.134 Epoch 51 Batch 7/16 train_loss = 0.133 Epoch 51 Batch 8/16 train_loss = 0.133 Epoch 51 Batch 9/16 train_loss = 0.132 Epoch 51 Batch 10/16 train_loss = 0.133 Epoch 51 Batch 11/16 train_loss = 0.135 Epoch 51 Batch 12/16 train_loss = 0.135 Epoch 51 Batch 13/16 train_loss = 0.133 Epoch 51 Batch 14/16 train_loss = 0.132 Epoch 51 Batch 15/16 train_loss = 0.132 Epoch 52 Batch 0/16 train_loss = 0.131 Epoch 52 Batch 1/16 train_loss = 0.131 Epoch 52 Batch 2/16 train_loss = 0.129 Epoch 52 Batch 3/16 train_loss = 0.129 Epoch 52 Batch 4/16 train_loss = 0.128 Epoch 52 Batch 5/16 train_loss = 0.127 Epoch 52 Batch 6/16 train_loss = 0.127 Epoch 52 Batch 7/16 train_loss = 0.126 Epoch 52 Batch 8/16 train_loss = 0.126 Epoch 52 Batch 9/16 train_loss = 0.125 Epoch 52 Batch 10/16 train_loss = 0.125 Epoch 52 Batch 11/16 train_loss = 0.125 Epoch 52 Batch 12/16 train_loss = 0.124 Epoch 52 Batch 13/16 train_loss = 0.123 Epoch 52 Batch 14/16 train_loss = 0.123 Epoch 52 Batch 15/16 train_loss = 0.122 Epoch 53 Batch 0/16 train_loss = 0.122 Epoch 53 Batch 1/16 train_loss = 0.121 Epoch 53 Batch 2/16 train_loss = 0.121 Epoch 53 Batch 3/16 train_loss = 0.120 Epoch 53 Batch 4/16 train_loss = 0.120 Epoch 53 Batch 5/16 train_loss = 0.119 Epoch 53 Batch 6/16 train_loss = 0.119 Epoch 53 Batch 7/16 train_loss = 0.118 Epoch 53 Batch 8/16 train_loss = 0.118 Epoch 53 Batch 9/16 train_loss = 0.118 Epoch 53 Batch 10/16 train_loss = 0.117 Epoch 53 Batch 11/16 train_loss = 0.117 Epoch 53 Batch 12/16 train_loss = 0.116 Epoch 53 Batch 13/16 train_loss = 0.116 Epoch 53 Batch 14/16 train_loss = 0.116 Epoch 53 Batch 15/16 train_loss = 0.115 Epoch 54 Batch 0/16 train_loss = 0.115 Epoch 54 Batch 1/16 train_loss = 0.115 Epoch 54 Batch 2/16 train_loss = 0.114 Epoch 54 Batch 3/16 train_loss = 0.114 Epoch 54 Batch 4/16 train_loss = 0.113 Epoch 54 Batch 5/16 train_loss = 0.113 Epoch 54 Batch 6/16 train_loss = 0.113 Epoch 54 Batch 7/16 train_loss = 0.112 Epoch 54 Batch 8/16 train_loss = 0.112 Epoch 54 Batch 9/16 train_loss = 0.112 Epoch 54 Batch 10/16 train_loss = 0.111 Epoch 54 Batch 11/16 train_loss = 0.111 Epoch 54 Batch 12/16 train_loss = 0.111 Epoch 54 Batch 13/16 train_loss = 0.110 Epoch 54 Batch 14/16 train_loss = 0.110 Epoch 54 Batch 15/16 train_loss = 0.110 Epoch 55 Batch 0/16 train_loss = 0.110 Epoch 55 Batch 1/16 train_loss = 0.109 Epoch 55 Batch 2/16 train_loss = 0.109 Epoch 55 Batch 3/16 train_loss = 0.109 Epoch 55 Batch 4/16 train_loss = 0.108 Epoch 55 Batch 5/16 train_loss = 0.108 Epoch 55 Batch 6/16 train_loss = 0.108 Epoch 55 Batch 7/16 train_loss = 0.108 Epoch 55 Batch 8/16 train_loss = 0.108 Epoch 55 Batch 9/16 train_loss = 0.107 Epoch 55 Batch 10/16 train_loss = 0.107 Epoch 55 Batch 11/16 train_loss = 0.106 Epoch 55 Batch 12/16 train_loss = 0.106 Epoch 55 Batch 13/16 train_loss = 0.106 Epoch 55 Batch 14/16 train_loss = 0.106 Epoch 55 Batch 15/16 train_loss = 0.105 Epoch 56 Batch 0/16 train_loss = 0.105 Epoch 56 Batch 1/16 train_loss = 0.105 Epoch 56 Batch 2/16 train_loss = 0.104 Epoch 56 Batch 3/16 train_loss = 0.105 Epoch 56 Batch 4/16 train_loss = 0.104 Epoch 56 Batch 5/16 train_loss = 0.104 Epoch 56 Batch 6/16 train_loss = 0.104 Epoch 56 Batch 7/16 train_loss = 0.103 Epoch 56 Batch 8/16 train_loss = 0.103 Epoch 56 Batch 9/16 train_loss = 0.104 Epoch 56 Batch 10/16 train_loss = 0.104 Epoch 56 Batch 11/16 train_loss = 0.103 Epoch 56 Batch 12/16 train_loss = 0.103 Epoch 56 Batch 13/16 train_loss = 0.102 Epoch 56 Batch 14/16 train_loss = 0.102 Epoch 56 Batch 15/16 train_loss = 0.102 Epoch 57 Batch 0/16 train_loss = 0.102 Epoch 57 Batch 1/16 train_loss = 0.102 Epoch 57 Batch 2/16 train_loss = 0.101 Epoch 57 Batch 3/16 train_loss = 0.101 Epoch 57 Batch 4/16 train_loss = 0.101 Epoch 57 Batch 5/16 train_loss = 0.101 Epoch 57 Batch 6/16 train_loss = 0.100 Epoch 57 Batch 7/16 train_loss = 0.100 Epoch 57 Batch 8/16 train_loss = 0.100 Epoch 57 Batch 9/16 train_loss = 0.100 Epoch 57 Batch 10/16 train_loss = 0.099 Epoch 57 Batch 11/16 train_loss = 0.099 Epoch 57 Batch 12/16 train_loss = 0.099 Epoch 57 Batch 13/16 train_loss = 0.099 Epoch 57 Batch 14/16 train_loss = 0.099 Epoch 57 Batch 15/16 train_loss = 0.099 Epoch 58 Batch 0/16 train_loss = 0.099 Epoch 58 Batch 1/16 train_loss = 0.098 Epoch 58 Batch 2/16 train_loss = 0.098 Epoch 58 Batch 3/16 train_loss = 0.098 Epoch 58 Batch 4/16 train_loss = 0.099 Epoch 58 Batch 5/16 train_loss = 0.099 Epoch 58 Batch 6/16 train_loss = 0.098 Epoch 58 Batch 7/16 train_loss = 0.098 Epoch 58 Batch 8/16 train_loss = 0.098 Epoch 58 Batch 9/16 train_loss = 0.097 Epoch 58 Batch 10/16 train_loss = 0.097 Epoch 58 Batch 11/16 train_loss = 0.098 Epoch 58 Batch 12/16 train_loss = 0.097 Epoch 58 Batch 13/16 train_loss = 0.097 Epoch 58 Batch 14/16 train_loss = 0.096 Epoch 58 Batch 15/16 train_loss = 0.096 Epoch 59 Batch 0/16 train_loss = 0.096 Epoch 59 Batch 1/16 train_loss = 0.096 Epoch 59 Batch 2/16 train_loss = 0.095 Epoch 59 Batch 3/16 train_loss = 0.095 Epoch 59 Batch 4/16 train_loss = 0.095 Epoch 59 Batch 5/16 train_loss = 0.095 Epoch 59 Batch 6/16 train_loss = 0.095 Epoch 59 Batch 7/16 train_loss = 0.095 Epoch 59 Batch 8/16 train_loss = 0.094 Epoch 59 Batch 9/16 train_loss = 0.094 Epoch 59 Batch 10/16 train_loss = 0.094 Epoch 59 Batch 11/16 train_loss = 0.094 Epoch 59 Batch 12/16 train_loss = 0.094 Epoch 59 Batch 13/16 train_loss = 0.094 Epoch 59 Batch 14/16 train_loss = 0.094 Epoch 59 Batch 15/16 train_loss = 0.093 Epoch 60 Batch 0/16 train_loss = 0.093 Epoch 60 Batch 1/16 train_loss = 0.093 Epoch 60 Batch 2/16 train_loss = 0.093 Epoch 60 Batch 3/16 train_loss = 0.093 Epoch 60 Batch 4/16 train_loss = 0.093 Epoch 60 Batch 5/16 train_loss = 0.093 Epoch 60 Batch 6/16 train_loss = 0.092 Epoch 60 Batch 7/16 train_loss = 0.092 Epoch 60 Batch 8/16 train_loss = 0.092 Epoch 60 Batch 9/16 train_loss = 0.092 Epoch 60 Batch 10/16 train_loss = 0.093 Epoch 60 Batch 11/16 train_loss = 0.094 Epoch 60 Batch 12/16 train_loss = 0.092 Epoch 60 Batch 13/16 train_loss = 0.093 Epoch 60 Batch 14/16 train_loss = 0.092 Epoch 60 Batch 15/16 train_loss = 0.092 Epoch 61 Batch 0/16 train_loss = 0.092 Epoch 61 Batch 1/16 train_loss = 0.092 Epoch 61 Batch 2/16 train_loss = 0.092 Epoch 61 Batch 3/16 train_loss = 0.091 Epoch 61 Batch 4/16 train_loss = 0.091 Epoch 61 Batch 5/16 train_loss = 0.091 Epoch 61 Batch 6/16 train_loss = 0.091 Epoch 61 Batch 7/16 train_loss = 0.091 Epoch 61 Batch 8/16 train_loss = 0.090 Epoch 61 Batch 9/16 train_loss = 0.090 Epoch 61 Batch 10/16 train_loss = 0.090 Epoch 61 Batch 11/16 train_loss = 0.090 Epoch 61 Batch 12/16 train_loss = 0.090 Epoch 61 Batch 13/16 train_loss = 0.090 Epoch 61 Batch 14/16 train_loss = 0.090 Epoch 61 Batch 15/16 train_loss = 0.090 Epoch 62 Batch 0/16 train_loss = 0.090 Epoch 62 Batch 1/16 train_loss = 0.089 Epoch 62 Batch 2/16 train_loss = 0.089 Epoch 62 Batch 3/16 train_loss = 0.089 Epoch 62 Batch 4/16 train_loss = 0.089 Epoch 62 Batch 5/16 train_loss = 0.089 Epoch 62 Batch 6/16 train_loss = 0.089 Epoch 62 Batch 7/16 train_loss = 0.089 Epoch 62 Batch 8/16 train_loss = 0.089 Epoch 62 Batch 9/16 train_loss = 0.089 Epoch 62 Batch 10/16 train_loss = 0.088 Epoch 62 Batch 11/16 train_loss = 0.088 Epoch 62 Batch 12/16 train_loss = 0.088 Epoch 62 Batch 13/16 train_loss = 0.088 Epoch 62 Batch 14/16 train_loss = 0.088 Epoch 62 Batch 15/16 train_loss = 0.088 Epoch 63 Batch 0/16 train_loss = 0.088 Epoch 63 Batch 1/16 train_loss = 0.088 Epoch 63 Batch 2/16 train_loss = 0.088 Epoch 63 Batch 3/16 train_loss = 0.088 Epoch 63 Batch 4/16 train_loss = 0.088 Epoch 63 Batch 5/16 train_loss = 0.088 Epoch 63 Batch 6/16 train_loss = 0.088 Epoch 63 Batch 7/16 train_loss = 0.087 Epoch 63 Batch 8/16 train_loss = 0.088 Epoch 63 Batch 9/16 train_loss = 0.087 Epoch 63 Batch 10/16 train_loss = 0.087 Epoch 63 Batch 11/16 train_loss = 0.088 Epoch 63 Batch 12/16 train_loss = 0.087 Epoch 63 Batch 13/16 train_loss = 0.087 Epoch 63 Batch 14/16 train_loss = 0.087 Epoch 63 Batch 15/16 train_loss = 0.087 Epoch 64 Batch 0/16 train_loss = 0.087 Epoch 64 Batch 1/16 train_loss = 0.087 Epoch 64 Batch 2/16 train_loss = 0.087 Epoch 64 Batch 3/16 train_loss = 0.087 Epoch 64 Batch 4/16 train_loss = 0.087 Epoch 64 Batch 5/16 train_loss = 0.087 Epoch 64 Batch 6/16 train_loss = 0.086 Epoch 64 Batch 7/16 train_loss = 0.086 Epoch 64 Batch 8/16 train_loss = 0.086 Epoch 64 Batch 9/16 train_loss = 0.086 Epoch 64 Batch 10/16 train_loss = 0.086 Epoch 64 Batch 11/16 train_loss = 0.086 Epoch 64 Batch 12/16 train_loss = 0.086 Epoch 64 Batch 13/16 train_loss = 0.086 Epoch 64 Batch 14/16 train_loss = 0.086 Epoch 64 Batch 15/16 train_loss = 0.085 Epoch 65 Batch 0/16 train_loss = 0.086 Epoch 65 Batch 1/16 train_loss = 0.086 Epoch 65 Batch 2/16 train_loss = 0.085 Epoch 65 Batch 3/16 train_loss = 0.085 Epoch 65 Batch 4/16 train_loss = 0.085 Epoch 65 Batch 5/16 train_loss = 0.085 Epoch 65 Batch 6/16 train_loss = 0.085 Epoch 65 Batch 7/16 train_loss = 0.085 Epoch 65 Batch 8/16 train_loss = 0.085 Epoch 65 Batch 9/16 train_loss = 0.085 Epoch 65 Batch 10/16 train_loss = 0.085 Epoch 65 Batch 11/16 train_loss = 0.085 Epoch 65 Batch 12/16 train_loss = 0.085 Epoch 65 Batch 13/16 train_loss = 0.084 Epoch 65 Batch 14/16 train_loss = 0.084 Epoch 65 Batch 15/16 train_loss = 0.084 Epoch 66 Batch 0/16 train_loss = 0.084 Epoch 66 Batch 1/16 train_loss = 0.084 Epoch 66 Batch 2/16 train_loss = 0.084 Epoch 66 Batch 3/16 train_loss = 0.084 Epoch 66 Batch 4/16 train_loss = 0.084 Epoch 66 Batch 5/16 train_loss = 0.084 Epoch 66 Batch 6/16 train_loss = 0.084 Epoch 66 Batch 7/16 train_loss = 0.084 Epoch 66 Batch 8/16 train_loss = 0.084 Epoch 66 Batch 9/16 train_loss = 0.084 Epoch 66 Batch 10/16 train_loss = 0.083 Epoch 66 Batch 11/16 train_loss = 0.083 Epoch 66 Batch 12/16 train_loss = 0.083 Epoch 66 Batch 13/16 train_loss = 0.083 Epoch 66 Batch 14/16 train_loss = 0.083 Epoch 66 Batch 15/16 train_loss = 0.083 Epoch 67 Batch 0/16 train_loss = 0.083 Epoch 67 Batch 1/16 train_loss = 0.083 Epoch 67 Batch 2/16 train_loss = 0.083 Epoch 67 Batch 3/16 train_loss = 0.083 Epoch 67 Batch 4/16 train_loss = 0.083 Epoch 67 Batch 5/16 train_loss = 0.083 Epoch 67 Batch 6/16 train_loss = 0.083 Epoch 67 Batch 7/16 train_loss = 0.083 Epoch 67 Batch 8/16 train_loss = 0.083 Epoch 67 Batch 9/16 train_loss = 0.083 Epoch 67 Batch 10/16 train_loss = 0.082 Epoch 67 Batch 11/16 train_loss = 0.082 Epoch 67 Batch 12/16 train_loss = 0.083 Epoch 67 Batch 13/16 train_loss = 0.082 Epoch 67 Batch 14/16 train_loss = 0.082 Epoch 67 Batch 15/16 train_loss = 0.082 Epoch 68 Batch 0/16 train_loss = 0.082 Epoch 68 Batch 1/16 train_loss = 0.082 Epoch 68 Batch 2/16 train_loss = 0.082 Epoch 68 Batch 3/16 train_loss = 0.082 Epoch 68 Batch 4/16 train_loss = 0.082 Epoch 68 Batch 5/16 train_loss = 0.082 Epoch 68 Batch 6/16 train_loss = 0.082 Epoch 68 Batch 7/16 train_loss = 0.082 Epoch 68 Batch 8/16 train_loss = 0.082 Epoch 68 Batch 9/16 train_loss = 0.082 Epoch 68 Batch 10/16 train_loss = 0.082 Epoch 68 Batch 11/16 train_loss = 0.082 Epoch 68 Batch 12/16 train_loss = 0.082 Epoch 68 Batch 13/16 train_loss = 0.082 Epoch 68 Batch 14/16 train_loss = 0.082 Epoch 68 Batch 15/16 train_loss = 0.081 Epoch 69 Batch 0/16 train_loss = 0.082 Epoch 69 Batch 1/16 train_loss = 0.081 Epoch 69 Batch 2/16 train_loss = 0.081 Epoch 69 Batch 3/16 train_loss = 0.081 Epoch 69 Batch 4/16 train_loss = 0.081 Epoch 69 Batch 5/16 train_loss = 0.081 Epoch 69 Batch 6/16 train_loss = 0.081 Epoch 69 Batch 7/16 train_loss = 0.081 Epoch 69 Batch 8/16 train_loss = 0.081 Epoch 69 Batch 9/16 train_loss = 0.081 Epoch 69 Batch 10/16 train_loss = 0.081 Epoch 69 Batch 11/16 train_loss = 0.081 Epoch 69 Batch 12/16 train_loss = 0.081 Epoch 69 Batch 13/16 train_loss = 0.081 Epoch 69 Batch 14/16 train_loss = 0.081 Epoch 69 Batch 15/16 train_loss = 0.081 Epoch 70 Batch 0/16 train_loss = 0.081 Epoch 70 Batch 1/16 train_loss = 0.081 Epoch 70 Batch 2/16 train_loss = 0.080 Epoch 70 Batch 3/16 train_loss = 0.080 Epoch 70 Batch 4/16 train_loss = 0.080 Epoch 70 Batch 5/16 train_loss = 0.080 Epoch 70 Batch 6/16 train_loss = 0.080 Epoch 70 Batch 7/16 train_loss = 0.080 Epoch 70 Batch 8/16 train_loss = 0.081 Epoch 70 Batch 9/16 train_loss = 0.080 Epoch 70 Batch 10/16 train_loss = 0.080 Epoch 70 Batch 11/16 train_loss = 0.080 Epoch 70 Batch 12/16 train_loss = 0.080 Epoch 70 Batch 13/16 train_loss = 0.080 Epoch 70 Batch 14/16 train_loss = 0.080 Epoch 70 Batch 15/16 train_loss = 0.080 Epoch 71 Batch 0/16 train_loss = 0.080 Epoch 71 Batch 1/16 train_loss = 0.080 Epoch 71 Batch 2/16 train_loss = 0.080 Epoch 71 Batch 3/16 train_loss = 0.080 Epoch 71 Batch 4/16 train_loss = 0.080 Epoch 71 Batch 5/16 train_loss = 0.080 Epoch 71 Batch 6/16 train_loss = 0.080 Epoch 71 Batch 7/16 train_loss = 0.080 Epoch 71 Batch 8/16 train_loss = 0.080 Epoch 71 Batch 9/16 train_loss = 0.080 Epoch 71 Batch 10/16 train_loss = 0.080 Epoch 71 Batch 11/16 train_loss = 0.080 Epoch 71 Batch 12/16 train_loss = 0.080 Epoch 71 Batch 13/16 train_loss = 0.080 Epoch 71 Batch 14/16 train_loss = 0.080 Epoch 71 Batch 15/16 train_loss = 0.079 Epoch 72 Batch 0/16 train_loss = 0.079 Epoch 72 Batch 1/16 train_loss = 0.079 Epoch 72 Batch 2/16 train_loss = 0.079 Epoch 72 Batch 3/16 train_loss = 0.079 Epoch 72 Batch 4/16 train_loss = 0.079 Epoch 72 Batch 5/16 train_loss = 0.079 Epoch 72 Batch 6/16 train_loss = 0.079 Epoch 72 Batch 7/16 train_loss = 0.079 Epoch 72 Batch 8/16 train_loss = 0.079 Epoch 72 Batch 9/16 train_loss = 0.079 Epoch 72 Batch 10/16 train_loss = 0.079 Epoch 72 Batch 11/16 train_loss = 0.079 Epoch 72 Batch 12/16 train_loss = 0.079 Epoch 72 Batch 13/16 train_loss = 0.079 Epoch 72 Batch 14/16 train_loss = 0.079 Epoch 72 Batch 15/16 train_loss = 0.079 Epoch 73 Batch 0/16 train_loss = 0.079 Epoch 73 Batch 1/16 train_loss = 0.079 Epoch 73 Batch 2/16 train_loss = 0.079 Epoch 73 Batch 3/16 train_loss = 0.079 Epoch 73 Batch 4/16 train_loss = 0.079 Epoch 73 Batch 5/16 train_loss = 0.079 Epoch 73 Batch 6/16 train_loss = 0.079 Epoch 73 Batch 7/16 train_loss = 0.079 Epoch 73 Batch 8/16 train_loss = 0.079 Epoch 73 Batch 9/16 train_loss = 0.079 Epoch 73 Batch 10/16 train_loss = 0.079 Epoch 73 Batch 11/16 train_loss = 0.078 Epoch 73 Batch 12/16 train_loss = 0.079 Epoch 73 Batch 13/16 train_loss = 0.078 Epoch 73 Batch 14/16 train_loss = 0.078 Epoch 73 Batch 15/16 train_loss = 0.078 Epoch 74 Batch 0/16 train_loss = 0.078 Epoch 74 Batch 1/16 train_loss = 0.078 Epoch 74 Batch 2/16 train_loss = 0.078 Epoch 74 Batch 3/16 train_loss = 0.078 Epoch 74 Batch 4/16 train_loss = 0.078 Epoch 74 Batch 5/16 train_loss = 0.078 Epoch 74 Batch 6/16 train_loss = 0.078 Epoch 74 Batch 7/16 train_loss = 0.078 Epoch 74 Batch 8/16 train_loss = 0.078 Epoch 74 Batch 9/16 train_loss = 0.078 Epoch 74 Batch 10/16 train_loss = 0.078 Epoch 74 Batch 11/16 train_loss = 0.078 Epoch 74 Batch 12/16 train_loss = 0.078 Epoch 74 Batch 13/16 train_loss = 0.078 Epoch 74 Batch 14/16 train_loss = 0.078 Epoch 74 Batch 15/16 train_loss = 0.078 Epoch 75 Batch 0/16 train_loss = 0.078 Epoch 75 Batch 1/16 train_loss = 0.078 Epoch 75 Batch 2/16 train_loss = 0.078 Epoch 75 Batch 3/16 train_loss = 0.078 Epoch 75 Batch 4/16 train_loss = 0.078 Epoch 75 Batch 5/16 train_loss = 0.078 Epoch 75 Batch 6/16 train_loss = 0.078 Epoch 75 Batch 7/16 train_loss = 0.078 Epoch 75 Batch 8/16 train_loss = 0.078 Epoch 75 Batch 9/16 train_loss = 0.078 Epoch 75 Batch 10/16 train_loss = 0.078 Epoch 75 Batch 11/16 train_loss = 0.078 Epoch 75 Batch 12/16 train_loss = 0.078 Epoch 75 Batch 13/16 train_loss = 0.078 Epoch 75 Batch 14/16 train_loss = 0.077 Epoch 75 Batch 15/16 train_loss = 0.077 Epoch 76 Batch 0/16 train_loss = 0.077 Epoch 76 Batch 1/16 train_loss = 0.077 Epoch 76 Batch 2/16 train_loss = 0.077 Epoch 76 Batch 3/16 train_loss = 0.077 Epoch 76 Batch 4/16 train_loss = 0.077 Epoch 76 Batch 5/16 train_loss = 0.077 Epoch 76 Batch 6/16 train_loss = 0.077 Epoch 76 Batch 7/16 train_loss = 0.077 Epoch 76 Batch 8/16 train_loss = 0.077 Epoch 76 Batch 9/16 train_loss = 0.077 Epoch 76 Batch 10/16 train_loss = 0.077 Epoch 76 Batch 11/16 train_loss = 0.077 Epoch 76 Batch 12/16 train_loss = 0.077 Epoch 76 Batch 13/16 train_loss = 0.077 Epoch 76 Batch 14/16 train_loss = 0.077 Epoch 76 Batch 15/16 train_loss = 0.077 Epoch 77 Batch 0/16 train_loss = 0.077 Epoch 77 Batch 1/16 train_loss = 0.077 Epoch 77 Batch 2/16 train_loss = 0.077 Epoch 77 Batch 3/16 train_loss = 0.077 Epoch 77 Batch 4/16 train_loss = 0.077 Epoch 77 Batch 5/16 train_loss = 0.077 Epoch 77 Batch 6/16 train_loss = 0.077 Epoch 77 Batch 7/16 train_loss = 0.077 Epoch 77 Batch 8/16 train_loss = 0.077 Epoch 77 Batch 9/16 train_loss = 0.077 Epoch 77 Batch 10/16 train_loss = 0.077 Epoch 77 Batch 11/16 train_loss = 0.077 Epoch 77 Batch 12/16 train_loss = 0.077 Epoch 77 Batch 13/16 train_loss = 0.077 Epoch 77 Batch 14/16 train_loss = 0.077 Epoch 77 Batch 15/16 train_loss = 0.077 Epoch 78 Batch 0/16 train_loss = 0.076 Epoch 78 Batch 1/16 train_loss = 0.076 Epoch 78 Batch 2/16 train_loss = 0.076 Epoch 78 Batch 3/16 train_loss = 0.077 Epoch 78 Batch 4/16 train_loss = 0.077 Epoch 78 Batch 5/16 train_loss = 0.077 Epoch 78 Batch 6/16 train_loss = 0.077 Epoch 78 Batch 7/16 train_loss = 0.077 Epoch 78 Batch 8/16 train_loss = 0.077 Epoch 78 Batch 9/16 train_loss = 0.077 Epoch 78 Batch 10/16 train_loss = 0.077 Epoch 78 Batch 11/16 train_loss = 0.077 Epoch 78 Batch 12/16 train_loss = 0.077 Epoch 78 Batch 13/16 train_loss = 0.077 Epoch 78 Batch 14/16 train_loss = 0.077 Epoch 78 Batch 15/16 train_loss = 0.077 Epoch 79 Batch 0/16 train_loss = 0.076 Epoch 79 Batch 1/16 train_loss = 0.076 Epoch 79 Batch 2/16 train_loss = 0.076 Epoch 79 Batch 3/16 train_loss = 0.076 Epoch 79 Batch 4/16 train_loss = 0.076 Epoch 79 Batch 5/16 train_loss = 0.076 Epoch 79 Batch 6/16 train_loss = 0.076 Epoch 79 Batch 7/16 train_loss = 0.076 Epoch 79 Batch 8/16 train_loss = 0.076 Epoch 79 Batch 9/16 train_loss = 0.076 Epoch 79 Batch 10/16 train_loss = 0.076 Epoch 79 Batch 11/16 train_loss = 0.076 Epoch 79 Batch 12/16 train_loss = 0.076 Epoch 79 Batch 13/16 train_loss = 0.076 Epoch 79 Batch 14/16 train_loss = 0.076 Epoch 79 Batch 15/16 train_loss = 0.076 Epoch 80 Batch 0/16 train_loss = 0.076 Epoch 80 Batch 1/16 train_loss = 0.076 Epoch 80 Batch 2/16 train_loss = 0.076 Epoch 80 Batch 3/16 train_loss = 0.076 Epoch 80 Batch 4/16 train_loss = 0.076 Epoch 80 Batch 5/16 train_loss = 0.076 Epoch 80 Batch 6/16 train_loss = 0.076 Epoch 80 Batch 7/16 train_loss = 0.076 Epoch 80 Batch 8/16 train_loss = 0.076 Epoch 80 Batch 9/16 train_loss = 0.076 Epoch 80 Batch 10/16 train_loss = 0.076 Epoch 80 Batch 11/16 train_loss = 0.076 Epoch 80 Batch 12/16 train_loss = 0.076 Epoch 80 Batch 13/16 train_loss = 0.076 Epoch 80 Batch 14/16 train_loss = 0.076 Epoch 80 Batch 15/16 train_loss = 0.076 Epoch 81 Batch 0/16 train_loss = 0.076 Epoch 81 Batch 1/16 train_loss = 0.076 Epoch 81 Batch 2/16 train_loss = 0.076 Epoch 81 Batch 3/16 train_loss = 0.075 Epoch 81 Batch 4/16 train_loss = 0.075 Epoch 81 Batch 5/16 train_loss = 0.075 Epoch 81 Batch 6/16 train_loss = 0.075 Epoch 81 Batch 7/16 train_loss = 0.075 Epoch 81 Batch 8/16 train_loss = 0.075 Epoch 81 Batch 9/16 train_loss = 0.075 Epoch 81 Batch 10/16 train_loss = 0.075 Epoch 81 Batch 11/16 train_loss = 0.075 Epoch 81 Batch 12/16 train_loss = 0.075 Epoch 81 Batch 13/16 train_loss = 0.075 Epoch 81 Batch 14/16 train_loss = 0.075 Epoch 81 Batch 15/16 train_loss = 0.075 Epoch 82 Batch 0/16 train_loss = 0.075 Epoch 82 Batch 1/16 train_loss = 0.075 Epoch 82 Batch 2/16 train_loss = 0.075 Epoch 82 Batch 3/16 train_loss = 0.075 Epoch 82 Batch 4/16 train_loss = 0.075 Epoch 82 Batch 5/16 train_loss = 0.075 Epoch 82 Batch 6/16 train_loss = 0.075 Epoch 82 Batch 7/16 train_loss = 0.075 Epoch 82 Batch 8/16 train_loss = 0.075 Epoch 82 Batch 9/16 train_loss = 0.075 Epoch 82 Batch 10/16 train_loss = 0.075 Epoch 82 Batch 11/16 train_loss = 0.075 Epoch 82 Batch 12/16 train_loss = 0.075 Epoch 82 Batch 13/16 train_loss = 0.075 Epoch 82 Batch 14/16 train_loss = 0.075 Epoch 82 Batch 15/16 train_loss = 0.075 Epoch 83 Batch 0/16 train_loss = 0.075 Epoch 83 Batch 1/16 train_loss = 0.075 Epoch 83 Batch 2/16 train_loss = 0.075 Epoch 83 Batch 3/16 train_loss = 0.075 Epoch 83 Batch 4/16 train_loss = 0.075 Epoch 83 Batch 5/16 train_loss = 0.075 Epoch 83 Batch 6/16 train_loss = 0.075 Epoch 83 Batch 7/16 train_loss = 0.075 Epoch 83 Batch 8/16 train_loss = 0.075 Epoch 83 Batch 9/16 train_loss = 0.075 Epoch 83 Batch 10/16 train_loss = 0.075 Epoch 83 Batch 11/16 train_loss = 0.075 Epoch 83 Batch 12/16 train_loss = 0.075 Epoch 83 Batch 13/16 train_loss = 0.075 Epoch 83 Batch 14/16 train_loss = 0.075 Epoch 83 Batch 15/16 train_loss = 0.075 Epoch 84 Batch 0/16 train_loss = 0.075 Epoch 84 Batch 1/16 train_loss = 0.075 Epoch 84 Batch 2/16 train_loss = 0.075 Epoch 84 Batch 3/16 train_loss = 0.075 Epoch 84 Batch 4/16 train_loss = 0.075 Epoch 84 Batch 5/16 train_loss = 0.075 Epoch 84 Batch 6/16 train_loss = 0.075 Epoch 84 Batch 7/16 train_loss = 0.075 Epoch 84 Batch 8/16 train_loss = 0.075 Epoch 84 Batch 9/16 train_loss = 0.075 Epoch 84 Batch 10/16 train_loss = 0.075 Epoch 84 Batch 11/16 train_loss = 0.075 Epoch 84 Batch 12/16 train_loss = 0.075 Epoch 84 Batch 13/16 train_loss = 0.075 Epoch 84 Batch 14/16 train_loss = 0.074 Epoch 84 Batch 15/16 train_loss = 0.074 Epoch 85 Batch 0/16 train_loss = 0.074 Epoch 85 Batch 1/16 train_loss = 0.074 Epoch 85 Batch 2/16 train_loss = 0.074 Epoch 85 Batch 3/16 train_loss = 0.074 Epoch 85 Batch 4/16 train_loss = 0.074 Epoch 85 Batch 5/16 train_loss = 0.074 Epoch 85 Batch 6/16 train_loss = 0.074 Epoch 85 Batch 7/16 train_loss = 0.074 Epoch 85 Batch 8/16 train_loss = 0.074 Epoch 85 Batch 9/16 train_loss = 0.074 Epoch 85 Batch 10/16 train_loss = 0.074 Epoch 85 Batch 11/16 train_loss = 0.074 Epoch 85 Batch 12/16 train_loss = 0.074 Epoch 85 Batch 13/16 train_loss = 0.074 Epoch 85 Batch 14/16 train_loss = 0.074 Epoch 85 Batch 15/16 train_loss = 0.074 Epoch 86 Batch 0/16 train_loss = 0.074 Epoch 86 Batch 1/16 train_loss = 0.074 Epoch 86 Batch 2/16 train_loss = 0.074 Epoch 86 Batch 3/16 train_loss = 0.075 Epoch 86 Batch 4/16 train_loss = 0.074 Epoch 86 Batch 5/16 train_loss = 0.074 Epoch 86 Batch 6/16 train_loss = 0.074 Epoch 86 Batch 7/16 train_loss = 0.074 Epoch 86 Batch 8/16 train_loss = 0.074 Epoch 86 Batch 9/16 train_loss = 0.074 Epoch 86 Batch 10/16 train_loss = 0.074 Epoch 86 Batch 11/16 train_loss = 0.074 Epoch 86 Batch 12/16 train_loss = 0.074 Epoch 86 Batch 13/16 train_loss = 0.074 Epoch 86 Batch 14/16 train_loss = 0.074 Epoch 86 Batch 15/16 train_loss = 0.074 Epoch 87 Batch 0/16 train_loss = 0.074 Epoch 87 Batch 1/16 train_loss = 0.074 Epoch 87 Batch 2/16 train_loss = 0.074 Epoch 87 Batch 3/16 train_loss = 0.074 Epoch 87 Batch 4/16 train_loss = 0.074 Epoch 87 Batch 5/16 train_loss = 0.074 Epoch 87 Batch 6/16 train_loss = 0.074 Epoch 87 Batch 7/16 train_loss = 0.074 Epoch 87 Batch 8/16 train_loss = 0.074 Epoch 87 Batch 9/16 train_loss = 0.074 Epoch 87 Batch 10/16 train_loss = 0.074 Epoch 87 Batch 11/16 train_loss = 0.074 Epoch 87 Batch 12/16 train_loss = 0.074 Epoch 87 Batch 13/16 train_loss = 0.074 Epoch 87 Batch 14/16 train_loss = 0.074 Epoch 87 Batch 15/16 train_loss = 0.074 Epoch 88 Batch 0/16 train_loss = 0.074 Epoch 88 Batch 1/16 train_loss = 0.074 Epoch 88 Batch 2/16 train_loss = 0.074 Epoch 88 Batch 3/16 train_loss = 0.074 Epoch 88 Batch 4/16 train_loss = 0.074 Epoch 88 Batch 5/16 train_loss = 0.074 Epoch 88 Batch 6/16 train_loss = 0.074 Epoch 88 Batch 7/16 train_loss = 0.074 Epoch 88 Batch 8/16 train_loss = 0.074 Epoch 88 Batch 9/16 train_loss = 0.074 Epoch 88 Batch 10/16 train_loss = 0.074 Epoch 88 Batch 11/16 train_loss = 0.074 Epoch 88 Batch 12/16 train_loss = 0.074 Epoch 88 Batch 13/16 train_loss = 0.074 Epoch 88 Batch 14/16 train_loss = 0.074 Epoch 88 Batch 15/16 train_loss = 0.074 Epoch 89 Batch 0/16 train_loss = 0.074 Epoch 89 Batch 1/16 train_loss = 0.074 Epoch 89 Batch 2/16 train_loss = 0.074 Epoch 89 Batch 3/16 train_loss = 0.074 Epoch 89 Batch 4/16 train_loss = 0.074 Epoch 89 Batch 5/16 train_loss = 0.074 Epoch 89 Batch 6/16 train_loss = 0.074 Epoch 89 Batch 7/16 train_loss = 0.074 Epoch 89 Batch 8/16 train_loss = 0.074 Epoch 89 Batch 9/16 train_loss = 0.074 Epoch 89 Batch 10/16 train_loss = 0.074 Epoch 89 Batch 11/16 train_loss = 0.074 Epoch 89 Batch 12/16 train_loss = 0.074 Epoch 89 Batch 13/16 train_loss = 0.074 Epoch 89 Batch 14/16 train_loss = 0.074 Epoch 89 Batch 15/16 train_loss = 0.074 Epoch 90 Batch 0/16 train_loss = 0.074 Epoch 90 Batch 1/16 train_loss = 0.073 Epoch 90 Batch 2/16 train_loss = 0.073 Epoch 90 Batch 3/16 train_loss = 0.074 Epoch 90 Batch 4/16 train_loss = 0.074 Epoch 90 Batch 5/16 train_loss = 0.074 Epoch 90 Batch 6/16 train_loss = 0.074 Epoch 90 Batch 7/16 train_loss = 0.074 Epoch 90 Batch 8/16 train_loss = 0.073 Epoch 90 Batch 9/16 train_loss = 0.073 Epoch 90 Batch 10/16 train_loss = 0.074 Epoch 90 Batch 11/16 train_loss = 0.073 Epoch 90 Batch 12/16 train_loss = 0.073 Epoch 90 Batch 13/16 train_loss = 0.074 Epoch 90 Batch 14/16 train_loss = 0.073 Epoch 90 Batch 15/16 train_loss = 0.073 Epoch 91 Batch 0/16 train_loss = 0.073 Epoch 91 Batch 1/16 train_loss = 0.073 Epoch 91 Batch 2/16 train_loss = 0.073 Epoch 91 Batch 3/16 train_loss = 0.073 Epoch 91 Batch 4/16 train_loss = 0.073 Epoch 91 Batch 5/16 train_loss = 0.073 Epoch 91 Batch 6/16 train_loss = 0.073 Epoch 91 Batch 7/16 train_loss = 0.073 Epoch 91 Batch 8/16 train_loss = 0.073 Epoch 91 Batch 9/16 train_loss = 0.073 Epoch 91 Batch 10/16 train_loss = 0.073 Epoch 91 Batch 11/16 train_loss = 0.073 Epoch 91 Batch 12/16 train_loss = 0.073 Epoch 91 Batch 13/16 train_loss = 0.073 Epoch 91 Batch 14/16 train_loss = 0.073 Epoch 91 Batch 15/16 train_loss = 0.073 Epoch 92 Batch 0/16 train_loss = 0.073 Epoch 92 Batch 1/16 train_loss = 0.073 Epoch 92 Batch 2/16 train_loss = 0.073 Epoch 92 Batch 3/16 train_loss = 0.073 Epoch 92 Batch 4/16 train_loss = 0.073 Epoch 92 Batch 5/16 train_loss = 0.073 Epoch 92 Batch 6/16 train_loss = 0.073 Epoch 92 Batch 7/16 train_loss = 0.073 Epoch 92 Batch 8/16 train_loss = 0.073 Epoch 92 Batch 9/16 train_loss = 0.073 Epoch 92 Batch 10/16 train_loss = 0.073 Epoch 92 Batch 11/16 train_loss = 0.073 Epoch 92 Batch 12/16 train_loss = 0.073 Epoch 92 Batch 13/16 train_loss = 0.073 Epoch 92 Batch 14/16 train_loss = 0.073 Epoch 92 Batch 15/16 train_loss = 0.073 Epoch 93 Batch 0/16 train_loss = 0.073 Epoch 93 Batch 1/16 train_loss = 0.073 Epoch 93 Batch 2/16 train_loss = 0.073 Epoch 93 Batch 3/16 train_loss = 0.073 Epoch 93 Batch 4/16 train_loss = 0.073 Epoch 93 Batch 5/16 train_loss = 0.073 Epoch 93 Batch 6/16 train_loss = 0.073 Epoch 93 Batch 7/16 train_loss = 0.073 Epoch 93 Batch 8/16 train_loss = 0.073 Epoch 93 Batch 9/16 train_loss = 0.073 Epoch 93 Batch 10/16 train_loss = 0.073 Epoch 93 Batch 11/16 train_loss = 0.073 Epoch 93 Batch 12/16 train_loss = 0.073 Epoch 93 Batch 13/16 train_loss = 0.073 Epoch 93 Batch 14/16 train_loss = 0.073 Epoch 93 Batch 15/16 train_loss = 0.073 Epoch 94 Batch 0/16 train_loss = 0.073 Epoch 94 Batch 1/16 train_loss = 0.073 Epoch 94 Batch 2/16 train_loss = 0.073 Epoch 94 Batch 3/16 train_loss = 0.073 Epoch 94 Batch 4/16 train_loss = 0.073 Epoch 94 Batch 5/16 train_loss = 0.073 Epoch 94 Batch 6/16 train_loss = 0.073 Epoch 94 Batch 7/16 train_loss = 0.073 Epoch 94 Batch 8/16 train_loss = 0.073 Epoch 94 Batch 9/16 train_loss = 0.073 Epoch 94 Batch 10/16 train_loss = 0.073 Epoch 94 Batch 11/16 train_loss = 0.073 Epoch 94 Batch 12/16 train_loss = 0.073 Epoch 94 Batch 13/16 train_loss = 0.073 Epoch 94 Batch 14/16 train_loss = 0.073 Epoch 94 Batch 15/16 train_loss = 0.073 Epoch 95 Batch 0/16 train_loss = 0.073 Epoch 95 Batch 1/16 train_loss = 0.073 Epoch 95 Batch 2/16 train_loss = 0.073 Epoch 95 Batch 3/16 train_loss = 0.073 Epoch 95 Batch 4/16 train_loss = 0.073 Epoch 95 Batch 5/16 train_loss = 0.073 Epoch 95 Batch 6/16 train_loss = 0.073 Epoch 95 Batch 7/16 train_loss = 0.073 Epoch 95 Batch 8/16 train_loss = 0.073 Epoch 95 Batch 9/16 train_loss = 0.073 Epoch 95 Batch 10/16 train_loss = 0.073 Epoch 95 Batch 11/16 train_loss = 0.073 Epoch 95 Batch 12/16 train_loss = 0.073 Epoch 95 Batch 13/16 train_loss = 0.073 Epoch 95 Batch 14/16 train_loss = 0.073 Epoch 95 Batch 15/16 train_loss = 0.073 Epoch 96 Batch 0/16 train_loss = 0.073 Epoch 96 Batch 1/16 train_loss = 0.073 Epoch 96 Batch 2/16 train_loss = 0.073 Epoch 96 Batch 3/16 train_loss = 0.073 Epoch 96 Batch 4/16 train_loss = 0.073 Epoch 96 Batch 5/16 train_loss = 0.073 Epoch 96 Batch 6/16 train_loss = 0.073 Epoch 96 Batch 7/16 train_loss = 0.073 Epoch 96 Batch 8/16 train_loss = 0.073 Epoch 96 Batch 9/16 train_loss = 0.073 Epoch 96 Batch 10/16 train_loss = 0.073 Epoch 96 Batch 11/16 train_loss = 0.073 Epoch 96 Batch 12/16 train_loss = 0.073 Epoch 96 Batch 13/16 train_loss = 0.073 Epoch 96 Batch 14/16 train_loss = 0.073 Epoch 96 Batch 15/16 train_loss = 0.073 Epoch 97 Batch 0/16 train_loss = 0.073 Epoch 97 Batch 1/16 train_loss = 0.073 Epoch 97 Batch 2/16 train_loss = 0.073 Epoch 97 Batch 3/16 train_loss = 0.073 Epoch 97 Batch 4/16 train_loss = 0.073 Epoch 97 Batch 5/16 train_loss = 0.073 Epoch 97 Batch 6/16 train_loss = 0.073 Epoch 97 Batch 7/16 train_loss = 0.073 Epoch 97 Batch 8/16 train_loss = 0.073 Epoch 97 Batch 9/16 train_loss = 0.073 Epoch 97 Batch 10/16 train_loss = 0.073 Epoch 97 Batch 11/16 train_loss = 0.072 Epoch 97 Batch 12/16 train_loss = 0.072 Epoch 97 Batch 13/16 train_loss = 0.072 Epoch 97 Batch 14/16 train_loss = 0.072 Epoch 97 Batch 15/16 train_loss = 0.072 Epoch 98 Batch 0/16 train_loss = 0.072 Epoch 98 Batch 1/16 train_loss = 0.072 Epoch 98 Batch 2/16 train_loss = 0.072 Epoch 98 Batch 3/16 train_loss = 0.072 Epoch 98 Batch 4/16 train_loss = 0.072 Epoch 98 Batch 5/16 train_loss = 0.072 Epoch 98 Batch 6/16 train_loss = 0.072 Epoch 98 Batch 7/16 train_loss = 0.072 Epoch 98 Batch 8/16 train_loss = 0.072 Epoch 98 Batch 9/16 train_loss = 0.072 Epoch 98 Batch 10/16 train_loss = 0.072 Epoch 98 Batch 11/16 train_loss = 0.072 Epoch 98 Batch 12/16 train_loss = 0.072 Epoch 98 Batch 13/16 train_loss = 0.072 Epoch 98 Batch 14/16 train_loss = 0.072 Epoch 98 Batch 15/16 train_loss = 0.072 Epoch 99 Batch 0/16 train_loss = 0.072 Epoch 99 Batch 1/16 train_loss = 0.072 Epoch 99 Batch 2/16 train_loss = 0.072 Epoch 99 Batch 3/16 train_loss = 0.072 Epoch 99 Batch 4/16 train_loss = 0.072 Epoch 99 Batch 5/16 train_loss = 0.072 Epoch 99 Batch 6/16 train_loss = 0.072 Epoch 99 Batch 7/16 train_loss = 0.072 Epoch 99 Batch 8/16 train_loss = 0.072 Epoch 99 Batch 9/16 train_loss = 0.072 Epoch 99 Batch 10/16 train_loss = 0.072 Epoch 99 Batch 11/16 train_loss = 0.072 Epoch 99 Batch 12/16 train_loss = 0.072 Epoch 99 Batch 13/16 train_loss = 0.072 Epoch 99 Batch 14/16 train_loss = 0.072 Epoch 99 Batch 15/16 train_loss = 0.072 Epoch 100 Batch 0/16 train_loss = 0.072 Epoch 100 Batch 1/16 train_loss = 0.072 Epoch 100 Batch 2/16 train_loss = 0.072 Epoch 100 Batch 3/16 train_loss = 0.072 Epoch 100 Batch 4/16 train_loss = 0.072 Epoch 100 Batch 5/16 train_loss = 0.072 Epoch 100 Batch 6/16 train_loss = 0.072 Epoch 100 Batch 7/16 train_loss = 0.072 Epoch 100 Batch 8/16 train_loss = 0.072 Epoch 100 Batch 9/16 train_loss = 0.072 Epoch 100 Batch 10/16 train_loss = 0.072 Epoch 100 Batch 11/16 train_loss = 0.072 Epoch 100 Batch 12/16 train_loss = 0.072 Epoch 100 Batch 13/16 train_loss = 0.072 Epoch 100 Batch 14/16 train_loss = 0.072 Epoch 100 Batch 15/16 train_loss = 0.072 Epoch 101 Batch 0/16 train_loss = 0.072 Epoch 101 Batch 1/16 train_loss = 0.072 Epoch 101 Batch 2/16 train_loss = 0.072 Epoch 101 Batch 3/16 train_loss = 0.072 Epoch 101 Batch 4/16 train_loss = 0.072 Epoch 101 Batch 5/16 train_loss = 0.072 Epoch 101 Batch 6/16 train_loss = 0.072 Epoch 101 Batch 7/16 train_loss = 0.072 Epoch 101 Batch 8/16 train_loss = 0.072 Epoch 101 Batch 9/16 train_loss = 0.072 Epoch 101 Batch 10/16 train_loss = 0.072 Epoch 101 Batch 11/16 train_loss = 0.072 Epoch 101 Batch 12/16 train_loss = 0.072 Epoch 101 Batch 13/16 train_loss = 0.072 Epoch 101 Batch 14/16 train_loss = 0.072 Epoch 101 Batch 15/16 train_loss = 0.072 Epoch 102 Batch 0/16 train_loss = 0.072 Epoch 102 Batch 1/16 train_loss = 0.072 Epoch 102 Batch 2/16 train_loss = 0.072 Epoch 102 Batch 3/16 train_loss = 0.072 Epoch 102 Batch 4/16 train_loss = 0.072 Epoch 102 Batch 5/16 train_loss = 0.072 Epoch 102 Batch 6/16 train_loss = 0.072 Epoch 102 Batch 7/16 train_loss = 0.072 Epoch 102 Batch 8/16 train_loss = 0.072 Epoch 102 Batch 9/16 train_loss = 0.072 Epoch 102 Batch 10/16 train_loss = 0.072 Epoch 102 Batch 11/16 train_loss = 0.072 Epoch 102 Batch 12/16 train_loss = 0.072 Epoch 102 Batch 13/16 train_loss = 0.072 Epoch 102 Batch 14/16 train_loss = 0.072 Epoch 102 Batch 15/16 train_loss = 0.072 Epoch 103 Batch 0/16 train_loss = 0.072 Epoch 103 Batch 1/16 train_loss = 0.072 Epoch 103 Batch 2/16 train_loss = 0.072 Epoch 103 Batch 3/16 train_loss = 0.072 Epoch 103 Batch 4/16 train_loss = 0.072 Epoch 103 Batch 5/16 train_loss = 0.072 Epoch 103 Batch 6/16 train_loss = 0.072 Epoch 103 Batch 7/16 train_loss = 0.072 Epoch 103 Batch 8/16 train_loss = 0.072 Epoch 103 Batch 9/16 train_loss = 0.072 Epoch 103 Batch 10/16 train_loss = 0.072 Epoch 103 Batch 11/16 train_loss = 0.072 Epoch 103 Batch 12/16 train_loss = 0.072 Epoch 103 Batch 13/16 train_loss = 0.072 Epoch 103 Batch 14/16 train_loss = 0.072 Epoch 103 Batch 15/16 train_loss = 0.072 Epoch 104 Batch 0/16 train_loss = 0.072 Epoch 104 Batch 1/16 train_loss = 0.072 Epoch 104 Batch 2/16 train_loss = 0.072 Epoch 104 Batch 3/16 train_loss = 0.072 Epoch 104 Batch 4/16 train_loss = 0.072 Epoch 104 Batch 5/16 train_loss = 0.072 Epoch 104 Batch 6/16 train_loss = 0.072 Epoch 104 Batch 7/16 train_loss = 0.072 Epoch 104 Batch 8/16 train_loss = 0.072 Epoch 104 Batch 9/16 train_loss = 0.072 Epoch 104 Batch 10/16 train_loss = 0.072 Epoch 104 Batch 11/16 train_loss = 0.072 Epoch 104 Batch 12/16 train_loss = 0.072 Epoch 104 Batch 13/16 train_loss = 0.072 Epoch 104 Batch 14/16 train_loss = 0.072 Epoch 104 Batch 15/16 train_loss = 0.072 Epoch 105 Batch 0/16 train_loss = 0.072 Epoch 105 Batch 1/16 train_loss = 0.072 Epoch 105 Batch 2/16 train_loss = 0.072 Epoch 105 Batch 3/16 train_loss = 0.072 Epoch 105 Batch 4/16 train_loss = 0.072 Epoch 105 Batch 5/16 train_loss = 0.072 Epoch 105 Batch 6/16 train_loss = 0.072 Epoch 105 Batch 7/16 train_loss = 0.072 Epoch 105 Batch 8/16 train_loss = 0.072 Epoch 105 Batch 9/16 train_loss = 0.072 Epoch 105 Batch 10/16 train_loss = 0.072 Epoch 105 Batch 11/16 train_loss = 0.072 Epoch 105 Batch 12/16 train_loss = 0.072 Epoch 105 Batch 13/16 train_loss = 0.072 Epoch 105 Batch 14/16 train_loss = 0.072 Epoch 105 Batch 15/16 train_loss = 0.072 Epoch 106 Batch 0/16 train_loss = 0.072 Epoch 106 Batch 1/16 train_loss = 0.072 Epoch 106 Batch 2/16 train_loss = 0.072 Epoch 106 Batch 3/16 train_loss = 0.072 Epoch 106 Batch 4/16 train_loss = 0.072 Epoch 106 Batch 5/16 train_loss = 0.072 Epoch 106 Batch 6/16 train_loss = 0.072 Epoch 106 Batch 7/16 train_loss = 0.072 Epoch 106 Batch 8/16 train_loss = 0.072 Epoch 106 Batch 9/16 train_loss = 0.072 Epoch 106 Batch 10/16 train_loss = 0.072 Epoch 106 Batch 11/16 train_loss = 0.072 Epoch 106 Batch 12/16 train_loss = 0.072 Epoch 106 Batch 13/16 train_loss = 0.072 Epoch 106 Batch 14/16 train_loss = 0.072 Epoch 106 Batch 15/16 train_loss = 0.072 Epoch 107 Batch 0/16 train_loss = 0.072 Epoch 107 Batch 1/16 train_loss = 0.072 Epoch 107 Batch 2/16 train_loss = 0.072 Epoch 107 Batch 3/16 train_loss = 0.072 Epoch 107 Batch 4/16 train_loss = 0.072 Epoch 107 Batch 5/16 train_loss = 0.072 Epoch 107 Batch 6/16 train_loss = 0.072 Epoch 107 Batch 7/16 train_loss = 0.072 Epoch 107 Batch 8/16 train_loss = 0.072 Epoch 107 Batch 9/16 train_loss = 0.072 Epoch 107 Batch 10/16 train_loss = 0.072 Epoch 107 Batch 11/16 train_loss = 0.072 Epoch 107 Batch 12/16 train_loss = 0.072 Epoch 107 Batch 13/16 train_loss = 0.072 Epoch 107 Batch 14/16 train_loss = 0.072 Epoch 107 Batch 15/16 train_loss = 0.072 Epoch 108 Batch 0/16 train_loss = 0.072 Epoch 108 Batch 1/16 train_loss = 0.072 Epoch 108 Batch 2/16 train_loss = 0.072 Epoch 108 Batch 3/16 train_loss = 0.072 Epoch 108 Batch 4/16 train_loss = 0.072 Epoch 108 Batch 5/16 train_loss = 0.071 Epoch 108 Batch 6/16 train_loss = 0.072 Epoch 108 Batch 7/16 train_loss = 0.072 Epoch 108 Batch 8/16 train_loss = 0.071 Epoch 108 Batch 9/16 train_loss = 0.071 Epoch 108 Batch 10/16 train_loss = 0.071 Epoch 108 Batch 11/16 train_loss = 0.071 Epoch 108 Batch 12/16 train_loss = 0.071 Epoch 108 Batch 13/16 train_loss = 0.071 Epoch 108 Batch 14/16 train_loss = 0.071 Epoch 108 Batch 15/16 train_loss = 0.071 Epoch 109 Batch 0/16 train_loss = 0.071 Epoch 109 Batch 1/16 train_loss = 0.071 Epoch 109 Batch 2/16 train_loss = 0.071 Epoch 109 Batch 3/16 train_loss = 0.071 Epoch 109 Batch 4/16 train_loss = 0.071 Epoch 109 Batch 5/16 train_loss = 0.071 Epoch 109 Batch 6/16 train_loss = 0.071 Epoch 109 Batch 7/16 train_loss = 0.071 Epoch 109 Batch 8/16 train_loss = 0.071 Epoch 109 Batch 9/16 train_loss = 0.071 Epoch 109 Batch 10/16 train_loss = 0.071 Epoch 109 Batch 11/16 train_loss = 0.071 Epoch 109 Batch 12/16 train_loss = 0.071 Epoch 109 Batch 13/16 train_loss = 0.071 Epoch 109 Batch 14/16 train_loss = 0.071 Epoch 109 Batch 15/16 train_loss = 0.071 Epoch 110 Batch 0/16 train_loss = 0.071 Epoch 110 Batch 1/16 train_loss = 0.071 Epoch 110 Batch 2/16 train_loss = 0.071 Epoch 110 Batch 3/16 train_loss = 0.071 Epoch 110 Batch 4/16 train_loss = 0.071 Epoch 110 Batch 5/16 train_loss = 0.071 Epoch 110 Batch 6/16 train_loss = 0.071 Epoch 110 Batch 7/16 train_loss = 0.071 Epoch 110 Batch 8/16 train_loss = 0.071 Epoch 110 Batch 9/16 train_loss = 0.071 Epoch 110 Batch 10/16 train_loss = 0.071 Epoch 110 Batch 11/16 train_loss = 0.071 Epoch 110 Batch 12/16 train_loss = 0.071 Epoch 110 Batch 13/16 train_loss = 0.072 Epoch 110 Batch 14/16 train_loss = 0.072 Epoch 110 Batch 15/16 train_loss = 0.072 Epoch 111 Batch 0/16 train_loss = 0.071 Epoch 111 Batch 1/16 train_loss = 0.071 Epoch 111 Batch 2/16 train_loss = 0.072 Epoch 111 Batch 3/16 train_loss = 0.071 Epoch 111 Batch 4/16 train_loss = 0.072 Epoch 111 Batch 5/16 train_loss = 0.072 Epoch 111 Batch 6/16 train_loss = 0.071 Epoch 111 Batch 7/16 train_loss = 0.071 Epoch 111 Batch 8/16 train_loss = 0.071 Epoch 111 Batch 9/16 train_loss = 0.071 Epoch 111 Batch 10/16 train_loss = 0.071 Epoch 111 Batch 11/16 train_loss = 0.071 Epoch 111 Batch 12/16 train_loss = 0.071 Epoch 111 Batch 13/16 train_loss = 0.071 Epoch 111 Batch 14/16 train_loss = 0.071 Epoch 111 Batch 15/16 train_loss = 0.071 Epoch 112 Batch 0/16 train_loss = 0.071 Epoch 112 Batch 1/16 train_loss = 0.071 Epoch 112 Batch 2/16 train_loss = 0.071 Epoch 112 Batch 3/16 train_loss = 0.071 Epoch 112 Batch 4/16 train_loss = 0.071 Epoch 112 Batch 5/16 train_loss = 0.071 Epoch 112 Batch 6/16 train_loss = 0.071 Epoch 112 Batch 7/16 train_loss = 0.071 Epoch 112 Batch 8/16 train_loss = 0.071 Epoch 112 Batch 9/16 train_loss = 0.071 Epoch 112 Batch 10/16 train_loss = 0.071 Epoch 112 Batch 11/16 train_loss = 0.071 Epoch 112 Batch 12/16 train_loss = 0.071 Epoch 112 Batch 13/16 train_loss = 0.071 Epoch 112 Batch 14/16 train_loss = 0.071 Epoch 112 Batch 15/16 train_loss = 0.071 Epoch 113 Batch 0/16 train_loss = 0.071 Epoch 113 Batch 1/16 train_loss = 0.071 Epoch 113 Batch 2/16 train_loss = 0.071 Epoch 113 Batch 3/16 train_loss = 0.071 Epoch 113 Batch 4/16 train_loss = 0.071 Epoch 113 Batch 5/16 train_loss = 0.071 Epoch 113 Batch 6/16 train_loss = 0.071 Epoch 113 Batch 7/16 train_loss = 0.071 Epoch 113 Batch 8/16 train_loss = 0.071 Epoch 113 Batch 9/16 train_loss = 0.071 Epoch 113 Batch 10/16 train_loss = 0.071 Epoch 113 Batch 11/16 train_loss = 0.071 Epoch 113 Batch 12/16 train_loss = 0.071 Epoch 113 Batch 13/16 train_loss = 0.071 Epoch 113 Batch 14/16 train_loss = 0.071 Epoch 113 Batch 15/16 train_loss = 0.071 Epoch 114 Batch 0/16 train_loss = 0.071 Epoch 114 Batch 1/16 train_loss = 0.071 Epoch 114 Batch 2/16 train_loss = 0.071 Epoch 114 Batch 3/16 train_loss = 0.071 Epoch 114 Batch 4/16 train_loss = 0.071 Epoch 114 Batch 5/16 train_loss = 0.071 Epoch 114 Batch 6/16 train_loss = 0.071 Epoch 114 Batch 7/16 train_loss = 0.071 Epoch 114 Batch 8/16 train_loss = 0.071 Epoch 114 Batch 9/16 train_loss = 0.071 Epoch 114 Batch 10/16 train_loss = 0.071 Epoch 114 Batch 11/16 train_loss = 0.071 Epoch 114 Batch 12/16 train_loss = 0.071 Epoch 114 Batch 13/16 train_loss = 0.071 Epoch 114 Batch 14/16 train_loss = 0.071 Epoch 114 Batch 15/16 train_loss = 0.071 Epoch 115 Batch 0/16 train_loss = 0.071 Epoch 115 Batch 1/16 train_loss = 0.071 Epoch 115 Batch 2/16 train_loss = 0.071 Epoch 115 Batch 3/16 train_loss = 0.071 Epoch 115 Batch 4/16 train_loss = 0.071 Epoch 115 Batch 5/16 train_loss = 0.071 Epoch 115 Batch 6/16 train_loss = 0.071 Epoch 115 Batch 7/16 train_loss = 0.071 Epoch 115 Batch 8/16 train_loss = 0.071 Epoch 115 Batch 9/16 train_loss = 0.071 Epoch 115 Batch 10/16 train_loss = 0.071 Epoch 115 Batch 11/16 train_loss = 0.071 Epoch 115 Batch 12/16 train_loss = 0.071 Epoch 115 Batch 13/16 train_loss = 0.071 Epoch 115 Batch 14/16 train_loss = 0.071 Epoch 115 Batch 15/16 train_loss = 0.071 Epoch 116 Batch 0/16 train_loss = 0.071 Epoch 116 Batch 1/16 train_loss = 0.071 Epoch 116 Batch 2/16 train_loss = 0.071 Epoch 116 Batch 3/16 train_loss = 0.071 Epoch 116 Batch 4/16 train_loss = 0.071 Epoch 116 Batch 5/16 train_loss = 0.071 Epoch 116 Batch 6/16 train_loss = 0.071 Epoch 116 Batch 7/16 train_loss = 0.071 Epoch 116 Batch 8/16 train_loss = 0.071 Epoch 116 Batch 9/16 train_loss = 0.071 Epoch 116 Batch 10/16 train_loss = 0.071 Epoch 116 Batch 11/16 train_loss = 0.071 Epoch 116 Batch 12/16 train_loss = 0.071 Epoch 116 Batch 13/16 train_loss = 0.071 Epoch 116 Batch 14/16 train_loss = 0.071 Epoch 116 Batch 15/16 train_loss = 0.071 Epoch 117 Batch 0/16 train_loss = 0.071 Epoch 117 Batch 1/16 train_loss = 0.071 Epoch 117 Batch 2/16 train_loss = 0.071 Epoch 117 Batch 3/16 train_loss = 0.071 Epoch 117 Batch 4/16 train_loss = 0.071 Epoch 117 Batch 5/16 train_loss = 0.071 Epoch 117 Batch 6/16 train_loss = 0.071 Epoch 117 Batch 7/16 train_loss = 0.071 Epoch 117 Batch 8/16 train_loss = 0.071 Epoch 117 Batch 9/16 train_loss = 0.071 Epoch 117 Batch 10/16 train_loss = 0.071 Epoch 117 Batch 11/16 train_loss = 0.071 Epoch 117 Batch 12/16 train_loss = 0.071 Epoch 117 Batch 13/16 train_loss = 0.071 Epoch 117 Batch 14/16 train_loss = 0.071 Epoch 117 Batch 15/16 train_loss = 0.071 Epoch 118 Batch 0/16 train_loss = 0.071 Epoch 118 Batch 1/16 train_loss = 0.071 Epoch 118 Batch 2/16 train_loss = 0.071 Epoch 118 Batch 3/16 train_loss = 0.071 Epoch 118 Batch 4/16 train_loss = 0.071 Epoch 118 Batch 5/16 train_loss = 0.071 Epoch 118 Batch 6/16 train_loss = 0.071 Epoch 118 Batch 7/16 train_loss = 0.071 Epoch 118 Batch 8/16 train_loss = 0.071 Epoch 118 Batch 9/16 train_loss = 0.071 Epoch 118 Batch 10/16 train_loss = 0.071 Epoch 118 Batch 11/16 train_loss = 0.071 Epoch 118 Batch 12/16 train_loss = 0.071 Epoch 118 Batch 13/16 train_loss = 0.071 Epoch 118 Batch 14/16 train_loss = 0.071 Epoch 118 Batch 15/16 train_loss = 0.071 Epoch 119 Batch 0/16 train_loss = 0.071 Epoch 119 Batch 1/16 train_loss = 0.071 Epoch 119 Batch 2/16 train_loss = 0.071 Epoch 119 Batch 3/16 train_loss = 0.071 Epoch 119 Batch 4/16 train_loss = 0.071 Epoch 119 Batch 5/16 train_loss = 0.071 Epoch 119 Batch 6/16 train_loss = 0.071 Epoch 119 Batch 7/16 train_loss = 0.071 Epoch 119 Batch 8/16 train_loss = 0.071 Epoch 119 Batch 9/16 train_loss = 0.071 Epoch 119 Batch 10/16 train_loss = 0.071 Epoch 119 Batch 11/16 train_loss = 0.071 Epoch 119 Batch 12/16 train_loss = 0.071 Epoch 119 Batch 13/16 train_loss = 0.071 Epoch 119 Batch 14/16 train_loss = 0.071 Epoch 119 Batch 15/16 train_loss = 0.071 Epoch 120 Batch 0/16 train_loss = 0.071 Epoch 120 Batch 1/16 train_loss = 0.071 Epoch 120 Batch 2/16 train_loss = 0.071 Epoch 120 Batch 3/16 train_loss = 0.071 Epoch 120 Batch 4/16 train_loss = 0.071 Epoch 120 Batch 5/16 train_loss = 0.071 Epoch 120 Batch 6/16 train_loss = 0.071 Epoch 120 Batch 7/16 train_loss = 0.071 Epoch 120 Batch 8/16 train_loss = 0.071 Epoch 120 Batch 9/16 train_loss = 0.071 Epoch 120 Batch 10/16 train_loss = 0.071 Epoch 120 Batch 11/16 train_loss = 0.071 Epoch 120 Batch 12/16 train_loss = 0.071 Epoch 120 Batch 13/16 train_loss = 0.071 Epoch 120 Batch 14/16 train_loss = 0.071 Epoch 120 Batch 15/16 train_loss = 0.071 Epoch 121 Batch 0/16 train_loss = 0.071 Epoch 121 Batch 1/16 train_loss = 0.071 Epoch 121 Batch 2/16 train_loss = 0.071 Epoch 121 Batch 3/16 train_loss = 0.071 Epoch 121 Batch 4/16 train_loss = 0.071 Epoch 121 Batch 5/16 train_loss = 0.071 Epoch 121 Batch 6/16 train_loss = 0.071 Epoch 121 Batch 7/16 train_loss = 0.071 Epoch 121 Batch 8/16 train_loss = 0.071 Epoch 121 Batch 9/16 train_loss = 0.071 Epoch 121 Batch 10/16 train_loss = 0.071 Epoch 121 Batch 11/16 train_loss = 0.071 Epoch 121 Batch 12/16 train_loss = 0.071 Epoch 121 Batch 13/16 train_loss = 0.071 Epoch 121 Batch 14/16 train_loss = 0.071 Epoch 121 Batch 15/16 train_loss = 0.071 Epoch 122 Batch 0/16 train_loss = 0.071 Epoch 122 Batch 1/16 train_loss = 0.071 Epoch 122 Batch 2/16 train_loss = 0.071 Epoch 122 Batch 3/16 train_loss = 0.071 Epoch 122 Batch 4/16 train_loss = 0.071 Epoch 122 Batch 5/16 train_loss = 0.071 Epoch 122 Batch 6/16 train_loss = 0.071 Epoch 122 Batch 7/16 train_loss = 0.071 Epoch 122 Batch 8/16 train_loss = 0.071 Epoch 122 Batch 9/16 train_loss = 0.071 Epoch 122 Batch 10/16 train_loss = 0.071 Epoch 122 Batch 11/16 train_loss = 0.071 Epoch 122 Batch 12/16 train_loss = 0.071 Epoch 122 Batch 13/16 train_loss = 0.071 Epoch 122 Batch 14/16 train_loss = 0.071 Epoch 122 Batch 15/16 train_loss = 0.071 Epoch 123 Batch 0/16 train_loss = 0.071 Epoch 123 Batch 1/16 train_loss = 0.071 Epoch 123 Batch 2/16 train_loss = 0.071 Epoch 123 Batch 3/16 train_loss = 0.071 Epoch 123 Batch 4/16 train_loss = 0.071 Epoch 123 Batch 5/16 train_loss = 0.071 Epoch 123 Batch 6/16 train_loss = 0.071 Epoch 123 Batch 7/16 train_loss = 0.071 Epoch 123 Batch 8/16 train_loss = 0.071 Epoch 123 Batch 9/16 train_loss = 0.071 Epoch 123 Batch 10/16 train_loss = 0.071 Epoch 123 Batch 11/16 train_loss = 0.071 Epoch 123 Batch 12/16 train_loss = 0.071 Epoch 123 Batch 13/16 train_loss = 0.071 Epoch 123 Batch 14/16 train_loss = 0.071 Epoch 123 Batch 15/16 train_loss = 0.071 Epoch 124 Batch 0/16 train_loss = 0.071 Epoch 124 Batch 1/16 train_loss = 0.071 Epoch 124 Batch 2/16 train_loss = 0.071 Epoch 124 Batch 3/16 train_loss = 0.071 Epoch 124 Batch 4/16 train_loss = 0.071 Epoch 124 Batch 5/16 train_loss = 0.071 Epoch 124 Batch 6/16 train_loss = 0.071 Epoch 124 Batch 7/16 train_loss = 0.071 Epoch 124 Batch 8/16 train_loss = 0.071 Epoch 124 Batch 9/16 train_loss = 0.071 Epoch 124 Batch 10/16 train_loss = 0.071 Epoch 124 Batch 11/16 train_loss = 0.071 Epoch 124 Batch 12/16 train_loss = 0.071 Epoch 124 Batch 13/16 train_loss = 0.071 Epoch 124 Batch 14/16 train_loss = 0.071 Epoch 124 Batch 15/16 train_loss = 0.071 Epoch 125 Batch 0/16 train_loss = 0.071 Epoch 125 Batch 1/16 train_loss = 0.071 Epoch 125 Batch 2/16 train_loss = 0.071 Epoch 125 Batch 3/16 train_loss = 0.071 Epoch 125 Batch 4/16 train_loss = 0.071 Epoch 125 Batch 5/16 train_loss = 0.071 Epoch 125 Batch 6/16 train_loss = 0.071 Epoch 125 Batch 7/16 train_loss = 0.071 Epoch 125 Batch 8/16 train_loss = 0.071 Epoch 125 Batch 9/16 train_loss = 0.071 Epoch 125 Batch 10/16 train_loss = 0.071 Epoch 125 Batch 11/16 train_loss = 0.071 Epoch 125 Batch 12/16 train_loss = 0.071 Epoch 125 Batch 13/16 train_loss = 0.071 Epoch 125 Batch 14/16 train_loss = 0.071 Epoch 125 Batch 15/16 train_loss = 0.071 Epoch 126 Batch 0/16 train_loss = 0.071 Epoch 126 Batch 1/16 train_loss = 0.071 Epoch 126 Batch 2/16 train_loss = 0.071 Epoch 126 Batch 3/16 train_loss = 0.071 Epoch 126 Batch 4/16 train_loss = 0.071 Epoch 126 Batch 5/16 train_loss = 0.071 Epoch 126 Batch 6/16 train_loss = 0.071 Epoch 126 Batch 7/16 train_loss = 0.071 Epoch 126 Batch 8/16 train_loss = 0.071 Epoch 126 Batch 9/16 train_loss = 0.071 Epoch 126 Batch 10/16 train_loss = 0.070 Epoch 126 Batch 11/16 train_loss = 0.070 Epoch 126 Batch 12/16 train_loss = 0.070 Epoch 126 Batch 13/16 train_loss = 0.070 Epoch 126 Batch 14/16 train_loss = 0.070 Epoch 126 Batch 15/16 train_loss = 0.070 Epoch 127 Batch 0/16 train_loss = 0.070 Epoch 127 Batch 1/16 train_loss = 0.070 Epoch 127 Batch 2/16 train_loss = 0.070 Epoch 127 Batch 3/16 train_loss = 0.070 Epoch 127 Batch 4/16 train_loss = 0.070 Epoch 127 Batch 5/16 train_loss = 0.070 Epoch 127 Batch 6/16 train_loss = 0.070 Epoch 127 Batch 7/16 train_loss = 0.070 Epoch 127 Batch 8/16 train_loss = 0.070 Epoch 127 Batch 9/16 train_loss = 0.070 Epoch 127 Batch 10/16 train_loss = 0.070 Epoch 127 Batch 11/16 train_loss = 0.070 Epoch 127 Batch 12/16 train_loss = 0.070 Epoch 127 Batch 13/16 train_loss = 0.070 Epoch 127 Batch 14/16 train_loss = 0.070 Epoch 127 Batch 15/16 train_loss = 0.070 Model Trained and Saved
Save seq_length
and save_dir
for generating a new TV script.
 Get the tensors using the following names:
Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
def get_tensors(loaded_graph):
"""
Get input, initial state, final state, and probabilities tensor from <loaded_graph>
:param loaded_graph: TensorFlow graph loaded from file
:return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
# TODO: Implement Function
inputTensor = loaded_graph.get_tensor_by_name('input:0')
initialStateTensor = loaded_graph.get_tensor_by_name('initial_state:0')
finalStateTensor = loaded_graph.get_tensor_by_name('final_state:0')
probsTensor = loaded_graph.get_tensor_by_name('probs:0')
return inputTensor, initialStateTensor, finalStateTensor, probsTensor
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_tensors(get_tensors)
Tests Passed
Implement the pick_word()
function to select the next word using probabilities
.
def pick_word(probabilities, int_to_vocab):
"""
Pick the next word in the generated text
:param probabilities: Probabilites of the next word
:param int_to_vocab: Dictionary of word ids as the keys and words as the values
:return: String of the predicted word
"""
# TODO: Implement Function
#probabilities = probabilities.tolist()
#word = int_to_vocab[probabilities.index(max(probabilities))]
word = np.random.choice(np.array(list(int_to_vocab.values())), 1, p=probabilities)
return ''.join(word)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_pick_word(pick_word)
Tests Passed
This will generate the TV script for you. Set gen_length
to the length of TV script you want to generate.
gen_length = 200
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_dir + '.meta')
loader.restore(sess, load_dir)
# Get Tensors from loaded model
input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
# Sentences generation setup
gen_sentences = [prime_word + ':']
prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
# Generate sentences
for n in range(gen_length):
# Dynamic Input
dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
dyn_seq_length = len(dyn_input[0])
# Get Prediction
probabilities, prev_state = sess.run(
[probs, final_state],
{input_text: dyn_input, initial_state: prev_state})
pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)
gen_sentences.append(pred_word)
# Remove tokens
tv_script = ' '.join(gen_sentences)
for key, token in token_dict.items():
ending = ' ' if key in ['\n', '(', '"'] else ''
tv_script = tv_script.replace(' ' + token.lower(), key)
tv_script = tv_script.replace('\n ', '\n')
tv_script = tv_script.replace('( ', '(')
print(tv_script)
moe_szyslak: dying through dying hans: cheated both both both gasp both service well of i victorious awkward deal ,, with have homer_simpson: of like bulletin(whether i close right your thirteen how'd can!, uh(p where gasp did! somebody shard(miles close right it's your mean gluten railroad gasp krusty can that's that okay the here exits shard a whether party i'm have this a just happily: girl counting ali deal with ,, that's) narrator: dad(barney_gumble: you i selma_bouvier: simpson(i dimly miles then cleveland fourth like bet, did old my uh of doesn't if moe carl_carlson: i it's moe carl_carlson: cigars c the nuts moe_szyslak: if house oh you whether dad(problems you language calendars nuclear moonnnnnnnn upon bowl using(cab(somebody bet whether! ?, for whether dad(hey and come barney_gumble: oh you off yeah(down and subject bet phone(ground(coaster the holiday the have in shard! it right your hey!, you well of moe_szyslak: can a got of! for usually(they they not could am
It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of another dataset. We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_tv_script_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Starbucks red cups are back today meaning the Christmas season has official begun for coffee fans
Coffee lovers rejoice - Starbucks is launching its festive red cups today.
by Katie BrooksIt's officially Christmas for coffee lovers as Starbucks launches it festive red cups.
Costa Coffee has already released its Christmas line-up, and now Starbucks is joining in the festive fun with their iconic red cups.
People love the Christmas event so much that there's even a dedicated website: countdowntoredcups.com , Hull Live reports.
With the return of the red cups also comes the Christmas menu featuring festive drinks old and new, as well as a range of food options.
Each store will also have a range of limited-edition reusable cups, each with a wintry theme, to help you do your bit for the enviroment.
New for 2019 is the Toasted Marshmallow Hot Chocolate, which you can pick up from £3.10.
All the usual suspects will be on the menu, including the Toffee Nut Latte and Eggnog Latte.
To get you in the festive mood, and fuel the Christmas shopping frenzy, look no further than the festive food range.
Tuck into a ‘Tis the Season Turkey Sandwich, Festive Feast Panini, and for dessert enjoy a Mince Pie, Christmas Tree Brownie, Twinkle and Sprinkle Caramel Cupcake or a sumptuous slice of the Christmas Bauble Chocolate Loaf Cake.
The Christmas range, including the red cups, are available for you to enjoy from November 5, 2019 until January 2, 2020.