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author
Mofan Zhou
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tf RNN2
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‎tensorflowTUT/tf20_RNN2/for_you_to_practice.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -23,7 +23,7 @@
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n_inputs = 28 # MNIST data input (img shape: 28*28)
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n_steps = 28 # time steps
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n_hidden_unis = 128 # neurons in hidden layer
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n_hidden_units = 128 # neurons in hidden layer
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n_classes = 10 # MNIST classes (0-9 digits)
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# tf Graph input
@@ -33,13 +33,13 @@
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# Define weights
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weights = {
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# (28, 128)
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'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_unis])),
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'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
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# (128, 10)
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'out': tf.Variable(tf.random_normal([n_hidden_unis, n_classes]))
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'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
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}
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biases = {
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# (128, )
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'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_unis, ])),
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'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
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# (10, )
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'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
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}

‎tensorflowTUT/tf20_RNN2/full_code.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -23,7 +23,7 @@
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n_inputs = 28 # MNIST data input (img shape: 28*28)
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n_steps = 28 # time steps
26-
n_hidden_unis = 128 # neurons in hidden layer
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n_hidden_units = 128 # neurons in hidden layer
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n_classes = 10 # MNIST classes (0-9 digits)
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# tf Graph input
@@ -33,13 +33,13 @@
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# Define weights
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weights = {
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# (28, 128)
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'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_unis])),
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'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
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# (128, 10)
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'out': tf.Variable(tf.random_normal([n_hidden_unis, n_classes]))
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'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
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}
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biases = {
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# (128, )
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'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_unis, ])),
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'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
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# (10, )
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'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
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}
@@ -57,13 +57,13 @@ def RNN(X, weights, biases):
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# X_in = (128 batch * 28 steps, 128 hidden)
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X_in = tf.matmul(X, weights['in']) + biases['in']
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# X_in ==> (128 batch, 28 steps, 128 hidden)
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X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_unis])
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X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
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# cell
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##########################################
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# basic LSTM Cell.
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lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis, forget_bias=1.0, state_is_tuple=True)
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lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True)
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# lstm cell is divided into two parts (c_state, m_state)
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_init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)
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