def feed_network(self,data,keep_prob,chunk_size,n_chunks,dynamic):
sequence_lengths = None
if dynamic:
sequence_lengths = net.calc_seqlenth(data if isinstance(data, tf.Tensor) else tf.stack(data))
weight_dropout1 = tf.nn.dropout(self._hidden_layer_1['weights'], keep_prob)
weight_dropout2 = tf.nn.dropout(self._hidden_layer_2['weights'], keep_prob)
output_dropout = tf.nn.dropout(self._output['weights'], keep_prob)
rnn_dropout1 = rnn.core_rnn_cell.DropoutWrapper(self._lstm_cell,output_keep_prob=keep_prob)
rnn_dropout2 = rnn.core_rnn_cell.DropoutWrapper(self._gru_cell,output_keep_prob=keep_prob)
batch_size = tf.shape(data)[0]
#begin Calculations
input_shape = data.get_shape().as_list()
ndim = len(input_shape)
axis = [1, 0] + list(range(2,ndim))
data = tf.transpose(data,(axis))
sequence = tf.unstack(data)
lstm_outputs, states = rnn.static_rnn(rnn_dropout1, sequence, dtype=tf.float32, sequence_length = sequence_lengths)
if dynamic:
lstm_outputs = tf.transpose(tf.stack(lstm_outputs), [1, 0, 2])
lstm_output = net.advanced_indexing_op(lstm_outputs, sequence_lengths)
else:
lstm_output = lstm_outputs[-1]
layer1 = tf.add(tf.matmul(lstm_output,weight_dropout1),self._hidden_layer_1['biases'])
layer1 = tf.nn.relu(layer1)
layer2 = tf.add(tf.matmul(layer1,weight_dropout2),self._hidden_layer_2['biases'])
layer2 = tf.nn.relu(layer2)
input_to_gru = tf.reshape(layer2,[batch_size,n_chunks,10])
ndim = input_to_gru.get_shape().as_list()
chunk_size = ndim[-1]
input_to_gru = tf.transpose(input_to_gru,[1,0,2])
input_to_gru = tf.reshape(input_to_gru,[-1,chunk_size])
input_to_gru = tf.split(input_to_gru,n_chunks,0)
gru_outputs, state = rnn.static_rnn(rnn_dropout2, input_to_gru, dtype=tf.float32, sequence_length = sequence_lengths)
if dynamic:
gru_outputs = tf.transpose(tf.stack(gru_outputs), [1, 0, 2])
gru_output = net.advanced_indexing_op(gru_outputs, sequence_lengths)
else:
gru_output = gru_outputs[-1]
output = tf.add(tf.matmul(gru_output,output_dropout),self._output['biases'])
return output
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