3h4

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3h4 / 6-2-dropout-multi-lstm.py

Created November 19, 2016 11:59

	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 / 6-1-add-dropout-multi-lstm.py

Created November 19, 2016 11:51

	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 / 5-5-dynamic-multi-lstm.py

Last active October 8, 2018 12:47

	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 / 5-4-softmax-dynamic.py

Created November 19, 2016 11:39

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

3h4 / 5-3-prediction-dynamic.py

Created November 19, 2016 11:37

	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 / 5-2-logits-labels-dynamic.py

Created November 18, 2016 22:49

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

3h4 / 5-1-forward-dynamic.py

Created November 18, 2016 22:45

	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 / 4-6-multi-lstm.py

Last active February 3, 2019 22:27

	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 / 4-5-forward-multilstm.py

Created November 18, 2016 22:31

	# 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 / 4-4-state-unpack-multilstm.py

Created November 18, 2016 22:29

	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)]
	)

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