def RNN(x, weights, biases):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2])
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(tensor=x, shape=[-1, n_input])
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(value=x, num_or_size_splits=n_steps, axis=0)
# Define a lstm cell with tensorflow
#lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1)
lstm_cell = rnn_cell.GRUCell(n_hidden)
#lstm_cell = rnn_cell.LSTMCell(n_hidden,use_peepholes=True)
# avoid overfitting
lstm_cell = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5)
# 2 layers lstm
lstm_cell = rnn_cell.MultiRNNCell([lstm_cell] * 2)
# Get lstm cell output
outputs, states = rnn.rnn(cell=lstm_cell, inputs=x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out'], outputs[-1]
def RNN(x, weights, biases, n_input):
x = tf.transpose(x, [1, 0, 2])
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(tensor=x, shape=[-1, n_input])
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(value=x, num_or_size_splits=n_steps, axis=0)
# Define a lstm cell with tensorflow
#lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1)
lstm_cell = rnn_cell.GRUCell(n_hidden)
#lstm_cell = rnn_cell.LSTMCell(n_hidden,use_peepholes=True)
# avoid overfitting
lstm_cell = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5)
# 2 layers lstm
# num_units = [256, 256]
# cells = [rnn_cell.GRUCell(num_units=n) for n in num_units]
# lstm_cell = rnn_cell.MultiRNNCell(cells)
lstm_cell = rnn_cell.MultiRNNCell([lstm_cell] * 2)
# Get lstm cell output
# print(x)
outputs, states = rnn.rnn(cell=lstm_cell, inputs=x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights) + biases, outputs