More tweaks (aee5a160) · Commits · 钟慕尧 / deepchem

deep_chem/models/deep3d.py

+9 −11

Original line number	Diff line number	Diff line
		@@ -38,14 +38,13 @@ def fit_3D_convolution(paths, task_types, task_transforms, axis_length=32, **tra
		"""
		Perform stochastic gradient descent for a 3D CNN.
		"""
		# TODO(rbharath): task_types is not yet used below.
		(X_train, y_train, W_train, train), (X_test, y_test, W_test, test) = process_3D_convolutions(
		paths, task_transforms)
		nb_classes = 2
		print "np.shape(y_train)"
		print np.shape(y_train)

		print "np.shape(X_train): " + str(np.shape(X_train))
		print "np.shape(y_train): " + str(np.shape(y_train))

		nb_classes = 2
		model = train_3D_convolution(X_train, y_train, axis_length, **training_params)
		results = eval_model(test, model, task_types,
		modeltype="keras", mode="tensor")
		@@ -63,21 +62,18 @@ def train_3D_convolution(X, y, axis_length=32, batch_size=50, nb_epoch=1):
		nb_epoch: int
		maximal number of epochs to run the optimizer
		"""
		print "train_3D_convolution"
		print "axis_length: " + str(axis_length)
		print "np.shape(X): " + str(np.shape(X))
		print "Shuffling X dimensions"
		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.
		(n_samples, axis_length, _, _, n_channels) = np.shape(X)
		# TODO(rbharath): Modify the featurization so that it matches desired shaped.
		X = np.reshape(X, (n_samples, axis_length, n_channels, axis_length, axis_length))
		print "np.shape(X): " + str(np.shape(X))
		print "Final shape of X: " + str(np.shape(X))
		# Number of classes for classification
		nb_classes = 2

		# number of convolutional filters to use at each layer
		nb_filters = [axis_length/2, axis_length, axis_length]
		print "nb_filters: " + str(nb_filters)

		# level of pooling to perform at each layer (POOL x POOL)
		nb_pool = [2, 2, 2]
		@@ -103,6 +99,8 @@ def train_3D_convolution(X, y, axis_length=32, batch_size=50, nb_epoch=1):
		model.add(Activation('relu'))
		model.add(MaxPooling3D(poolsize=(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, 32/2, init='normal'))
		model.add(Activation('relu'))
		model.add(Dropout(0.5))

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