fixed issue with border mode. (d4519b34) · Commits · 钟慕尧 / deepchem

deep_chem/models/init.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -14,7 +14,6 @@ class Model(object):
		def __init__(self, task_types, model_params, initialize_raw_model=True):
		self.task_types = task_types
		self.model_params = model_params
		self.raw_model = None

		def fit_on_batch(self, X, y, w):
		"""

deep_chem/models/deep3d.py

+17 −20

Original line number	Diff line number	Diff line
		@@ -13,6 +13,17 @@ from keras.layers.core import Dense, Dropout, Activation, Flatten
		from keras.layers.convolutional import Convolution3D, MaxPooling3D
		from deep_chem.models import Model

		def shuffle_shape(shape):
		(axis_length, _, _, n_channels) = shape
		shuffled_shape = (n_channels, axis_length, axis_length, axis_length)
		return shuffled_shape

		def shuffle_data(X):
		(n_samples, axis_length, _, _, n_channels) = np.shape(X)
		X = np.reshape(X, (n_samples, n_channels, axis_length, axis_length, axis_length))
		return X


		class DockingDNN(Model):
		"""
		Wrapper class for fitting 3D convolutional networks for deep docking.
		@@ -39,25 +50,24 @@ class DockingDNN(Model):

		model.add(Convolution3D(nb_filter=nb_filters[0], nb_depth=nb_conv[0],
		nb_row=nb_conv[0], nb_col=nb_conv[0],
		input_shape=self.input_shape))
		input_shape=self.input_shape, border_mode="full"))
		model.add(Activation('relu'))

		model.add(MaxPooling3D(pool_size=(nb_pool[0], nb_pool[0], nb_pool[0])))
		model.add(Convolution3D(nb_filter=nb_filters[1], nb_depth=nb_conv[1],
		nb_row=nb_conv[1], nb_col=nb_conv[1]))
		nb_row=nb_conv[1], nb_col=nb_conv[1], border_mode="full"))
		model.add(Activation('relu'))
		model.add(MaxPooling3D(pool_size=(nb_pool[1], nb_pool[1], nb_pool[1])))
		model.add(Convolution3D(nb_filter=nb_filters[2], nb_depth=nb_conv[2],
		nb_row=nb_conv[2], nb_col=nb_conv[2]))
		nb_row=nb_conv[2], nb_col=nb_conv[2], border_mode="full"))
		model.add(Activation('relu'))
		model.add(MaxPooling3D(pool_size=(nb_pool[2], nb_pool[2], nb_pool[2])))
		model.add(Flatten())
		# TODO(rbharath): If we change away from axis-size 32, this code will break.
		# Eventually figure out a more general rule that works for all axis sizes.
		model.add(Dense(32/2, init='normal'))
		model.add(Dense(16, init='normal'))
		model.add(Activation('relu'))
		model.add(Dropout(0.5))
		# TODO(rbharath): Generalize this to support classification as well as regression.
		model.add(Dense(1, init='normal'))

		sgd = RMSprop(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
		@@ -65,29 +75,16 @@ class DockingDNN(Model):
		model.compile(loss=loss_function, optimizer=sgd)
		self.raw_model = model

		def shuffle_data(self, X):
		(n_samples, axis_length, _, _, n_channels) = np.shape(X)
		X = np.reshape(X, (n_samples, n_channels, axis_length, axis_length, axis_length))
		return X


		def fit_on_batch(self, X, y, w):
		print("Training 3D model")
		print("Original shape of X: " + str(np.shape(X)))
		print("Shuffling X dimensions to match convnet")
		# TODO(rbharath): Modify the featurization so that it matches desired shaped.
		X = self.shuffle_data(X)
		print("Final shape of X: " + str(np.shape(X)))

		print("About to fit data to model.")
		X = shuffle_data(X)
		self.raw_model.train_on_batch(X, y)
		print("Finished training on batch.")

		def predict_on_batch(self, X):
		if len(np.shape(X)) != 5:
		raise ValueError(
		"Tensorial datatype must be of shape (n_samples, N, N, N, n_channels).")
		X = self.shuffle_data(X)
		X = shuffle_data(X)
		y_pred = self.raw_model.predict_on_batch(X)
		y_pred = np.squeeze(y_pred)
		return y_pred

deep_chem/models/model.py

+8 −4

Original line number	Diff line number	Diff line
		@@ -11,16 +11,20 @@ from deep_chem.models.deep import MultiTaskDNN
		from deep_chem.models.deep3d import DockingDNN
		from deep_chem.models.standard import SklearnModel

		def model_builder(model_type, task_types, model_params):
		def model_builder(model_type, task_types, model_params,
		initialize_raw_model=True):
		"""
		Factory function to construct model.
		"""
		if model_type == "singletask_deep_network":
		model = SingleTaskDNN(task_types, model_params)
		model = SingleTaskDNN(task_types, model_params,
		initialize_raw_model)
		elif model_type == "multitask_deep_network":
		model = MultiTaskDNN(task_types, model_params)
		model = MultiTaskDNN(task_types, model_params,
		initialize_raw_model)
		elif model_type == "convolutional_3D_regressor":
		model = DockingDNN(task_types, model_params)
		model = DockingDNN(task_types, model_params,
		initialize_raw_model)
		else:
		model = SklearnModel(task_types, model_params)
		return model

deep_chem/scripts/modeler.py

+0 −2

Original line number	Diff line number	Diff line
		@@ -310,9 +310,7 @@ def featurize_inputs_wrapper(args):
		def train_test_split_wrapper(args):
		"""Wrapper function that calls _train_test_split_wrapper after unwrapping args."""
		train_test_split(args.paths, args.output_transforms,

		args.input_transforms, args.feature_types,

		args.splittype, args.mode, args.data_dir)

		def fit_model_wrapper(args):

deep_chem/utils/evaluate.py

+19 −16

Original line number	Diff line number	Diff line
		@@ -45,8 +45,11 @@ def compute_y_pred(model, data_dir, csv_out, split):
		column_names = ['ids'] + task_names + pred_task_names + w_task_names
		pred_y_df = pd.DataFrame(columns=column_names)

		for _, row in metadata_df.iterrows():
		if row['split'] == split:
		split_df = metadata_df.loc[metadata_df['split'] == split]
		nb_batch = split_df.shape[0]

		for i, row in split_df.iterrows():
		print("Evaluating on %s batch %d out of %d" % (split, i+1, nb_batch))
		X = load_sharded_dataset(row['X'])
		y = load_sharded_dataset(row['y'])
		w = load_sharded_dataset(row['w'])

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