Loading deepchem/models/tensorgraph/layers.py +34 −17 Original line number Diff line number Diff line import random import string from collections import Sequence import tensorflow as tf import numpy as np Loading @@ -21,6 +22,8 @@ class Layer(object): self.tensorboard = kwargs['tensorboard'] if in_layers is None: in_layers = list() if not isinstance(in_layers, Sequence): in_layers = [in_layers] self.in_layers = in_layers self.op_type = "gpu" self.variables = [] Loading Loading @@ -77,6 +80,8 @@ class Layer(object): """ if in_layers is None: in_layers = self.in_layers if not isinstance(in_layers, Sequence): in_layers = [in_layers] tensors = [] for input in in_layers: if isinstance(input, tf.Tensor): Loading Loading @@ -205,7 +210,7 @@ class Dense(Layer): else: biases_initializer = self.biases_initializer() if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( out_tensor = tf.contrib.layers.fully_connected( parent, num_outputs=self.out_channels, activation_fn=self.activation_fn, Loading @@ -214,7 +219,7 @@ class Dense(Layer): scope=self._get_scope_name(), reuse=self._reuse, trainable=True) return self.out_tensor else: dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=self.activation_fn, Loading @@ -226,7 +231,7 @@ class Dense(Layer): out_tensor = tf.map_fn(dense_fn, parent) if set_tensors: self.variables = tf.get_collection( tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope_name) tf.GraphKeys.GLOBAL_VARIABLES, scope=self._get_scope_name()) self.out_tensor = out_tensor return out_tensor Loading Loading @@ -341,10 +346,24 @@ class Repeat(Layer): class GRU(Layer): """A Gated Recurrent Unit. This layer expects its input to be of shape (batch_size, sequence_length, ...). It consists of a set of independent sequence (one for each element in the batch), that are each propagated independently through the GRU. """ def __init__(self, n_hidden, out_channels, batch_size, **kwargs): def __init__(self, n_hidden, batch_size, **kwargs): """Create a Gated Recurrent Unit. Parameters ---------- n_hidden: int the size of the GRU's hidden state, which also determines the size of its output batch_size: int the batch size that will be used with this layer """ self.n_hidden = n_hidden self.out_channels = out_channels self.batch_size = batch_size super(GRU, self).__init__(**kwargs) Loading @@ -355,13 +374,11 @@ class GRU(Layer): parent_tensor = inputs[0] gru_cell = tf.contrib.rnn.GRUCell(self.n_hidden) initial_gru_state = gru_cell.zero_state(self.batch_size, tf.float32) rnn_outputs, rnn_states = tf.nn.dynamic_rnn( out_tensor, rnn_states = tf.nn.dynamic_rnn( gru_cell, parent_tensor, initial_state=initial_gru_state, scope=self.name) projection = lambda x: tf.contrib.layers.linear(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) out_tensor = tf.map_fn(projection, rnn_outputs) if set_tensors: self.out_tensor = out_tensor return out_tensor Loading Loading
deepchem/models/tensorgraph/layers.py +34 −17 Original line number Diff line number Diff line import random import string from collections import Sequence import tensorflow as tf import numpy as np Loading @@ -21,6 +22,8 @@ class Layer(object): self.tensorboard = kwargs['tensorboard'] if in_layers is None: in_layers = list() if not isinstance(in_layers, Sequence): in_layers = [in_layers] self.in_layers = in_layers self.op_type = "gpu" self.variables = [] Loading Loading @@ -77,6 +80,8 @@ class Layer(object): """ if in_layers is None: in_layers = self.in_layers if not isinstance(in_layers, Sequence): in_layers = [in_layers] tensors = [] for input in in_layers: if isinstance(input, tf.Tensor): Loading Loading @@ -205,7 +210,7 @@ class Dense(Layer): else: biases_initializer = self.biases_initializer() if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( out_tensor = tf.contrib.layers.fully_connected( parent, num_outputs=self.out_channels, activation_fn=self.activation_fn, Loading @@ -214,7 +219,7 @@ class Dense(Layer): scope=self._get_scope_name(), reuse=self._reuse, trainable=True) return self.out_tensor else: dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=self.activation_fn, Loading @@ -226,7 +231,7 @@ class Dense(Layer): out_tensor = tf.map_fn(dense_fn, parent) if set_tensors: self.variables = tf.get_collection( tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope_name) tf.GraphKeys.GLOBAL_VARIABLES, scope=self._get_scope_name()) self.out_tensor = out_tensor return out_tensor Loading Loading @@ -341,10 +346,24 @@ class Repeat(Layer): class GRU(Layer): """A Gated Recurrent Unit. This layer expects its input to be of shape (batch_size, sequence_length, ...). It consists of a set of independent sequence (one for each element in the batch), that are each propagated independently through the GRU. """ def __init__(self, n_hidden, out_channels, batch_size, **kwargs): def __init__(self, n_hidden, batch_size, **kwargs): """Create a Gated Recurrent Unit. Parameters ---------- n_hidden: int the size of the GRU's hidden state, which also determines the size of its output batch_size: int the batch size that will be used with this layer """ self.n_hidden = n_hidden self.out_channels = out_channels self.batch_size = batch_size super(GRU, self).__init__(**kwargs) Loading @@ -355,13 +374,11 @@ class GRU(Layer): parent_tensor = inputs[0] gru_cell = tf.contrib.rnn.GRUCell(self.n_hidden) initial_gru_state = gru_cell.zero_state(self.batch_size, tf.float32) rnn_outputs, rnn_states = tf.nn.dynamic_rnn( out_tensor, rnn_states = tf.nn.dynamic_rnn( gru_cell, parent_tensor, initial_state=initial_gru_state, scope=self.name) projection = lambda x: tf.contrib.layers.linear(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) out_tensor = tf.map_fn(projection, rnn_outputs) if set_tensors: self.out_tensor = out_tensor return out_tensor Loading
