Loading deepchem/models/tensorgraph/layers.py +31 −2 Original line number Diff line number Diff line Loading @@ -146,6 +146,31 @@ class Dense(Layer): scope_name=None, reuse=False, **kwargs): """Create a dense layer. The weight and bias initializers are specified by callable objects that construct and return a Tensorflow initializer when invoked with no arguments. This will typically be either the initializer class itself (if the constructor does not require arguments), or a TFWrapper (if it does). Parameters ---------- out_channels: int the number of output values activation_fn: object the Tensorflow activation function to apply to the output biases_initializer: callable object the initializer for bias values. This may be None, in which case the layer will not include biases. weights_initializer: callable object the initializer for weight values time_series: bool if True, the dense layer is applied to each element of a batch in sequence scope_name: str an optional scope name for the layer's variables reuse: bool whether or not the layer and its variables should be reused """ super(Dense, self).__init__(**kwargs) self.out_channels = out_channels self.out_tensor = None Loading @@ -165,12 +190,16 @@ class Dense(Layer): if len(in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % in_layers) parent = in_layers[0] if self.biases_initializer is None: biases_initializer = None else: biases_initializer = self.biases_initializer() if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, num_outputs=self.out_channels, activation_fn=self.activation_fn, biases_initializer=self.biases_initializer(), biases_initializer=biases_initializer, weights_initializer=self.weights_initializer(), scope=self.scope_name, reuse=self.reuse, Loading @@ -179,7 +208,7 @@ class Dense(Layer): dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=self.activation_fn, biases_initializer=self.biases_initializer(), biases_initializer=biases_initializer, weights_initializer=self.weights_initializer(), scope=self.scope_name, reuse=self.reuse, Loading Loading
deepchem/models/tensorgraph/layers.py +31 −2 Original line number Diff line number Diff line Loading @@ -146,6 +146,31 @@ class Dense(Layer): scope_name=None, reuse=False, **kwargs): """Create a dense layer. The weight and bias initializers are specified by callable objects that construct and return a Tensorflow initializer when invoked with no arguments. This will typically be either the initializer class itself (if the constructor does not require arguments), or a TFWrapper (if it does). Parameters ---------- out_channels: int the number of output values activation_fn: object the Tensorflow activation function to apply to the output biases_initializer: callable object the initializer for bias values. This may be None, in which case the layer will not include biases. weights_initializer: callable object the initializer for weight values time_series: bool if True, the dense layer is applied to each element of a batch in sequence scope_name: str an optional scope name for the layer's variables reuse: bool whether or not the layer and its variables should be reused """ super(Dense, self).__init__(**kwargs) self.out_channels = out_channels self.out_tensor = None Loading @@ -165,12 +190,16 @@ class Dense(Layer): if len(in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % in_layers) parent = in_layers[0] if self.biases_initializer is None: biases_initializer = None else: biases_initializer = self.biases_initializer() if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, num_outputs=self.out_channels, activation_fn=self.activation_fn, biases_initializer=self.biases_initializer(), biases_initializer=biases_initializer, weights_initializer=self.weights_initializer(), scope=self.scope_name, reuse=self.reuse, Loading @@ -179,7 +208,7 @@ class Dense(Layer): dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=self.activation_fn, biases_initializer=self.biases_initializer(), biases_initializer=biases_initializer, weights_initializer=self.weights_initializer(), scope=self.scope_name, reuse=self.reuse, Loading
