3h4

from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2

3h4

cell = tf.nn.rnn_cell.LSTMCell(state_size, state_is_tuple=True)
cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.5)
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)

3h4

from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2

3h4

losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)

3h4

logits_series = tf.unpack(tf.reshape(logits, [batch_size, truncated_backprop_length, num_classes]), axis=1)
predictions_series = [tf.nn.softmax(logit) for logit in logits_list]

3h4

logits = tf.matmul(states_series, W2) + b2 #Broadcasted addition
labels = tf.reshape(batchY_placeholder, [-1])

3h4

states_series, current_state = tf.nn.dynamic_rnn(cell, tf.expand_dims(batchX_placeholder, -1), initial_state=rnn_tuple_state)
states_series = tf.reshape(states_series, [-1, state_size])

3h4

from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2

3h4

# Forward passes
cell = tf.nn.rnn_cell.LSTMCell(state_size, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
states_series, current_state = tf.nn.rnn(cell, inputs_series, initial_state=rnn_tuple_state)

3h4

state_per_layer_list = tf.unpack(init_state, axis=0)
rnn_tuple_state = tuple(
    [tf.nn.rnn_cell.LSTMStateTuple(state_per_layer_list[idx][0], state_per_layer_list[idx][1])
     for idx in range(num_layers)]
)