Merge branch 'master' into hdf5-diskdataset-metadata (e19323f2) · Commits · 钟慕尧 / deepchem

Dockerfile

+2 −3

Original line number	Diff line number	Diff line
		@@ -2,7 +2,7 @@ FROM nvidia/cuda

		# Install some utilities
		RUN apt-get update && \
		apt-get install -y -q wget git libxrender1 && \
		apt-get install -y -q wget git libxrender1 libsm6 && \
		apt-get clean

		# Install miniconda
		@@ -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

README.md

+5 −544

File changed.

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deepchem/models/tensorflow_models/lr.py

+1 −3

Original line number	Diff line number	Diff line
		@@ -243,8 +243,6 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		raise ValueError('Unrecognized rank combination for output: %s' %
		(batch_output.shape,))
		output.append(batch_output)

		outputs = np.array(
		from_one_hot(np.squeeze(np.concatenate(output)), axis=-1))
		outputs = np.array(from_one_hot(np.concatenate(output), axis=-1))

		return np.copy(outputs)

deepchem/models/tensorflow_models/robust_multitask.py

+8 −10

Original line number	Diff line number	Diff line
		@@ -160,15 +160,12 @@ class RobustMultitaskClassifier(TensorflowMultiTaskClassifier):
		task_layer_size = layer_sizes[-1]
		print("Adding output weights of shape %s" % str([task_layer_size, 1]))
		output.append(
		tf.squeeze(
		model_ops.logits(
		task_layer,
		num_classes=2,
		weight_init=tf.truncated_normal(
		shape=[task_layer_size, 2],
		stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(
		value=bias_init_consts[-1], shape=[2]))))
		shape=[task_layer_size, 2], stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(value=bias_init_consts[-1], shape=[2])))
		return (output, labels, weights)


		@@ -328,5 +325,6 @@ class RobustMultitaskRegressor(TensorflowMultiTaskRegressor):
		shape=[task_layer_size, 1],
		stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(
		value=bias_init_consts[-1], shape=[1]))))
		value=bias_init_consts[-1], shape=[1])),
		axis=1))
		return (output, labels, weights)

deepchem/models/tensorgraph/layers.py

+62 −5

Original line number	Diff line number	Diff line
		@@ -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.

		@@ -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:
		@@ -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)
		@@ -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.
		@@ -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
		@@ -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
		@@ -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
		@@ -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.
		@@ -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
		@@ -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
		@@ -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
		@@ -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.
		@@ -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
		@@ -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
		@@ -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
		@@ -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.
		@@ -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
		@@ -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
		@@ -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

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