Loading Dockerfile +1 −2 Original line number Diff line number Diff line Loading @@ -21,10 +21,9 @@ ENV PATH /miniconda/bin:$PATH # TODO: Get rid of this when there is a stable release of deepchem. RUN git clone https://github.com/deepchem/deepchem.git && \ cd deepchem && \ git checkout tags/1.2.0 && \ git checkout tags/1.3.0 && \ sed -i -- 's/tensorflow$/tensorflow-gpu/g' scripts/install_deepchem_conda.sh && \ bash scripts/install_deepchem_conda.sh root && \ pip install tensorflow-gpu==1.0.1 && \ python setup.py develop # Clean up Loading README.md +1 −1 Original line number Diff line number Diff line Loading @@ -68,7 +68,7 @@ via this installation procedure. ### Easy Install via Conda ```bash conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.2.0 conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.3.0 ``` ### Installing Dependencies Manually Loading deepchem/models/tensorgraph/layers.py +62 −5 Original line number Diff line number Diff line Loading @@ -376,6 +376,8 @@ class Conv1D(Layer): stride=1, padding='SAME', activation_fn=tf.nn.relu, biases_initializer=tf.random_normal_initializer, weights_initializer=tf.random_normal_initializer, **kwargs): """Create a Conv1D layer. Loading @@ -391,12 +393,19 @@ class Conv1D(Layer): the padding method to use, either 'SAME' or 'VALID' 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 """ self.width = width self.out_channels = out_channels self.stride = stride self.padding = padding self.activation_fn = activation_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer self.out_tensor = None super(Conv1D, self).__init__(**kwargs) try: Loading @@ -416,11 +425,15 @@ class Conv1D(Layer): raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.get_shape() parent_channel_size = parent_shape[2].value f = tf.Variable( tf.random_normal([self.width, parent_channel_size, self.out_channels])) b = tf.Variable(tf.random_normal([self.out_channels])) f = tf.Variable(self.weights_initializer() ([self.width, parent_channel_size, self.out_channels])) t = tf.nn.conv1d(parent, f, stride=self.stride, padding=self.padding) if self.biases_initializer is not None: b = tf.Variable(self.biases_initializer()([self.out_channels])) t = tf.nn.bias_add(t, b) if self.activation_fn is None: out_tensor = t else: out_tensor = self.activation_fn(t) if set_tensors: self._record_variable_scope(self.name) Loading Loading @@ -1464,6 +1477,8 @@ class Conv2D(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv2D layer. Loading @@ -1486,6 +1501,11 @@ class Conv2D(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1493,6 +1513,8 @@ class Conv2D(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv2D, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1522,6 +1544,8 @@ class Conv2D(SharedVariableScope): padding=self.padding, activation_fn=self.activation_fn, normalizer_fn=self.normalizer_fn, biases_initializer=self.biases_initializer(), weights_initializer=self.weights_initializer(), scope=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1552,6 +1576,8 @@ class Conv3D(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv3D layer. Loading @@ -1574,6 +1600,11 @@ class Conv3D(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1581,6 +1612,8 @@ class Conv3D(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv3D, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1611,6 +1644,8 @@ class Conv3D(SharedVariableScope): padding=self.padding, activation=self.activation_fn, activity_regularizer=self.normalizer_fn, bias_initializer=self.biases_initializer(), kernel_initializer=self.weights_initializer(), name=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1642,6 +1677,8 @@ class Conv2DTranspose(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv2DTranspose layer. Loading @@ -1664,6 +1701,11 @@ class Conv2DTranspose(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1671,6 +1713,8 @@ class Conv2DTranspose(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv2DTranspose, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1700,6 +1744,8 @@ class Conv2DTranspose(SharedVariableScope): padding=self.padding, activation_fn=self.activation_fn, normalizer_fn=self.normalizer_fn, biases_initializer=self.biases_initializer(), weights_initializer=self.weights_initializer(), scope=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1730,6 +1776,8 @@ class Conv3DTranspose(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv3DTranspose layer. Loading @@ -1752,6 +1800,11 @@ class Conv3DTranspose(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1759,6 +1812,8 @@ class Conv3DTranspose(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv3DTranspose, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1789,6 +1844,8 @@ class Conv3DTranspose(SharedVariableScope): padding=self.padding, activation=self.activation_fn, activity_regularizer=self.normalizer_fn, bias_initializer=self.biases_initializer(), kernel_initializer=self.weights_initializer(), name=self._get_scope_name(), reuse=reuse) break Loading devtools/conda-recipe/deepchem/meta.yaml +8 −6 Original line number Diff line number Diff line package: name: {{ environ.get('package_name', 'deepchem') }} version: "1.2.0" version: "1.3.0" source: git_url: https://github.com/deepchem/deepchem.git git_tag: 1.2.0 git_tag: 1.3.0 build: number: 0 Loading @@ -22,8 +22,9 @@ requirements: - python {{ environ.get('python_version', '3.5') }} - pdbfixer ==1.4 - rdkit ==2017.03.1 - mdtraj - joblib - icu ==56.1 - mdtraj ==1.8.0 - joblib ==0.11 - scikit-learn ==0.18.1 - keras ==1.2.2 - networkx ==1.11 Loading @@ -31,9 +32,10 @@ requirements: - pillow ==4.2.1 - pandas ==0.19.2 - {{ environ.get('tensorflow_enabled','tensorflow') }} ==1.3.0 - nose - nose-timer - nose ==1.3.7 - nose-timer ==0.7.0 - flaky ==3.3.0 - zlib ==1.2.11 test: Loading Loading
Dockerfile +1 −2 Original line number Diff line number Diff line Loading @@ -21,10 +21,9 @@ ENV PATH /miniconda/bin:$PATH # TODO: Get rid of this when there is a stable release of deepchem. RUN git clone https://github.com/deepchem/deepchem.git && \ cd deepchem && \ git checkout tags/1.2.0 && \ git checkout tags/1.3.0 && \ sed -i -- 's/tensorflow$/tensorflow-gpu/g' scripts/install_deepchem_conda.sh && \ bash scripts/install_deepchem_conda.sh root && \ pip install tensorflow-gpu==1.0.1 && \ python setup.py develop # Clean up Loading
README.md +1 −1 Original line number Diff line number Diff line Loading @@ -68,7 +68,7 @@ via this installation procedure. ### Easy Install via Conda ```bash conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.2.0 conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.3.0 ``` ### Installing Dependencies Manually Loading
deepchem/models/tensorgraph/layers.py +62 −5 Original line number Diff line number Diff line Loading @@ -376,6 +376,8 @@ class Conv1D(Layer): stride=1, padding='SAME', activation_fn=tf.nn.relu, biases_initializer=tf.random_normal_initializer, weights_initializer=tf.random_normal_initializer, **kwargs): """Create a Conv1D layer. Loading @@ -391,12 +393,19 @@ class Conv1D(Layer): the padding method to use, either 'SAME' or 'VALID' 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 """ self.width = width self.out_channels = out_channels self.stride = stride self.padding = padding self.activation_fn = activation_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer self.out_tensor = None super(Conv1D, self).__init__(**kwargs) try: Loading @@ -416,11 +425,15 @@ class Conv1D(Layer): raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.get_shape() parent_channel_size = parent_shape[2].value f = tf.Variable( tf.random_normal([self.width, parent_channel_size, self.out_channels])) b = tf.Variable(tf.random_normal([self.out_channels])) f = tf.Variable(self.weights_initializer() ([self.width, parent_channel_size, self.out_channels])) t = tf.nn.conv1d(parent, f, stride=self.stride, padding=self.padding) if self.biases_initializer is not None: b = tf.Variable(self.biases_initializer()([self.out_channels])) t = tf.nn.bias_add(t, b) if self.activation_fn is None: out_tensor = t else: out_tensor = self.activation_fn(t) if set_tensors: self._record_variable_scope(self.name) Loading Loading @@ -1464,6 +1477,8 @@ class Conv2D(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv2D layer. Loading @@ -1486,6 +1501,11 @@ class Conv2D(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1493,6 +1513,8 @@ class Conv2D(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv2D, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1522,6 +1544,8 @@ class Conv2D(SharedVariableScope): padding=self.padding, activation_fn=self.activation_fn, normalizer_fn=self.normalizer_fn, biases_initializer=self.biases_initializer(), weights_initializer=self.weights_initializer(), scope=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1552,6 +1576,8 @@ class Conv3D(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv3D layer. Loading @@ -1574,6 +1600,11 @@ class Conv3D(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1581,6 +1612,8 @@ class Conv3D(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv3D, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1611,6 +1644,8 @@ class Conv3D(SharedVariableScope): padding=self.padding, activation=self.activation_fn, activity_regularizer=self.normalizer_fn, bias_initializer=self.biases_initializer(), kernel_initializer=self.weights_initializer(), name=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1642,6 +1677,8 @@ class Conv2DTranspose(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv2DTranspose layer. Loading @@ -1664,6 +1701,11 @@ class Conv2DTranspose(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1671,6 +1713,8 @@ class Conv2DTranspose(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv2DTranspose, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1700,6 +1744,8 @@ class Conv2DTranspose(SharedVariableScope): padding=self.padding, activation_fn=self.activation_fn, normalizer_fn=self.normalizer_fn, biases_initializer=self.biases_initializer(), weights_initializer=self.weights_initializer(), scope=self._get_scope_name(), reuse=reuse) break Loading Loading @@ -1730,6 +1776,8 @@ class Conv3DTranspose(SharedVariableScope): padding='SAME', activation_fn=tf.nn.relu, normalizer_fn=None, biases_initializer=tf.zeros_initializer, weights_initializer=tf.contrib.layers.xavier_initializer, scope_name=None, **kwargs): """Create a Conv3DTranspose layer. Loading @@ -1752,6 +1800,11 @@ class Conv3DTranspose(SharedVariableScope): the Tensorflow activation function to apply to the output normalizer_fn: object the Tensorflow normalizer 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 """ self.num_outputs = num_outputs self.kernel_size = kernel_size Loading @@ -1759,6 +1812,8 @@ class Conv3DTranspose(SharedVariableScope): self.padding = padding self.activation_fn = activation_fn self.normalizer_fn = normalizer_fn self.weights_initializer = weights_initializer self.biases_initializer = biases_initializer super(Conv3DTranspose, self).__init__(**kwargs) if scope_name is None: scope_name = self.name Loading Loading @@ -1789,6 +1844,8 @@ class Conv3DTranspose(SharedVariableScope): padding=self.padding, activation=self.activation_fn, activity_regularizer=self.normalizer_fn, bias_initializer=self.biases_initializer(), kernel_initializer=self.weights_initializer(), name=self._get_scope_name(), reuse=reuse) break Loading
devtools/conda-recipe/deepchem/meta.yaml +8 −6 Original line number Diff line number Diff line package: name: {{ environ.get('package_name', 'deepchem') }} version: "1.2.0" version: "1.3.0" source: git_url: https://github.com/deepchem/deepchem.git git_tag: 1.2.0 git_tag: 1.3.0 build: number: 0 Loading @@ -22,8 +22,9 @@ requirements: - python {{ environ.get('python_version', '3.5') }} - pdbfixer ==1.4 - rdkit ==2017.03.1 - mdtraj - joblib - icu ==56.1 - mdtraj ==1.8.0 - joblib ==0.11 - scikit-learn ==0.18.1 - keras ==1.2.2 - networkx ==1.11 Loading @@ -31,9 +32,10 @@ requirements: - pillow ==4.2.1 - pandas ==0.19.2 - {{ environ.get('tensorflow_enabled','tensorflow') }} ==1.3.0 - nose - nose-timer - nose ==1.3.7 - nose-timer ==0.7.0 - flaky ==3.3.0 - zlib ==1.2.11 test: Loading
