Add self.pad_batches parameter to Tensorflow models; tests passing (c96af58e) · Commits · 钟慕尧 / deepchem

deepchem/models/models.py

+3 −10

Original line number	Diff line number	Diff line
		@@ -19,7 +19,6 @@ import sklearn

		from deepchem.data import Dataset, pad_features
		from deepchem.trans import undo_transforms
		from deepchem.trans import undo_grad_transforms
		from deepchem.utils.save import load_from_disk
		from deepchem.utils.save import save_to_disk
		from deepchem.utils.save import log
		@@ -58,7 +57,7 @@ class Model(object):
		raise NotImplementedError(
		"Each model is responsible for its own fit_on_batch method.")

		def predict_on_batch(self, X, pad_batches=False):
		def predict_on_batch(self, X):
		"""
		Makes predictions on given batch of new data.

		@@ -66,14 +65,11 @@ class Model(object):
		----------
		X: np.ndarray
		Features
		pad_batch: bool, optional
		Ignored for Sklearn Model. Only used for Tensorflow models
		with rigid batch-size requirements.
		"""
		raise NotImplementedError(
		"Each model is responsible for its own predict_on_batch method.")

		def predict_proba_on_batch(self, X, pad_batches=False):
		def predict_proba_on_batch(self, X):
		"""
		Makes predictions of class probabilities on given batch of new data.

		@@ -81,9 +77,6 @@ class Model(object):
		----------
		X: np.ndarray
		Features
		pad_batch: bool, optional
		Ignored for Sklearn Model. Only used for Tensorflow models
		with rigid batch-size requirements.
		"""
		raise NotImplementedError(
		"Each model is responsible for its own predict_on_batch method.")
		@@ -126,7 +119,7 @@ class Model(object):
		log("Starting epoch %s" % str(epoch+1), self.verbose)
		losses = []
		for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(
		batch_size, pad_batches=pad_batches):
		batch_size):
		losses.append(self.fit_on_batch(X_batch, y_batch, w_batch))
		log("Avg loss for epoch %d: %f"
		% (epoch+1,np.array(losses).mean()),self.verbose)

deepchem/models/tensorflow_models/init.py

+19 −18

Original line number	Diff line number	Diff line
		@@ -17,7 +17,9 @@ from deepchem.models import Model
		from deepchem.metrics import from_one_hot
		from deepchem.nn import model_ops
		from deepchem.models.tensorflow_models import utils as tf_utils
		from deepchem.trans import undo_transforms
		from deepchem.utils.save import log
		from deepchem.utils.evaluate import Evaluator
		from deepchem.data import pad_features
		from tensorflow.contrib.layers.python.layers import batch_norm

		@@ -109,7 +111,7 @@ class TensorflowGraphModel(Model):
		weight_init_stddevs=[.02], bias_init_consts=[1.], penalty=0.0,
		penalty_type="l2", dropouts=[0.5], learning_rate=.001,
		momentum=.9, optimizer="adam", batch_size=50, n_classes=2,
		verbose=True, seed=None, **kwargs):
		pad_batches=False, verbose=True, seed=None, **kwargs):
		"""Constructs the computational graph.

		This function constructs the computational graph for the model. It relies
		@@ -166,6 +168,7 @@ class TensorflowGraphModel(Model):
		self.optimizer = optimizer
		self.batch_size = batch_size
		self.n_classes = n_classes
		self.pad_batches = pad_batches
		self.verbose= verbose
		self.seed = seed

		@@ -270,8 +273,8 @@ class TensorflowGraphModel(Model):

		return loss

		def fit(self, dataset, nb_epoch=10, pad_batch=False,
		max_checkpoints_to_keep=5, log_every_N_batches=50, **kwargs):
		def fit(self, dataset, nb_epoch=10, max_checkpoints_to_keep=5,
		log_every_N_batches=50, **kwargs):
		"""Fit the model.

		Parameters
		@@ -280,8 +283,6 @@ class TensorflowGraphModel(Model):
		Dataset object holding training data
		nb_epoch: 10
		Number of training epochs.
		pad_batches: bool
		Whether or not to pad each batch to exactly be of size batch_size.
		max_checkpoints_to_keep: int
		Maximum number of checkpoints to keep; older checkpoints will be deleted.
		log_every_N_batches: int
		@@ -311,7 +312,7 @@ class TensorflowGraphModel(Model):
		# Turns out there are valid cases where we don't want pad-batches
		# on by default.
		#dataset.iterbatches(batch_size, pad_batches=True)):
		dataset.iterbatches(self.batch_size, pad_batch=pad_batch)):
		dataset.iterbatches(self.batch_size, pad_batches=self.pad_batches)):
		if ind % log_every_N_batches == 0:
		log("On batch %d" % ind, self.verbose)
		# Run training op.
		@@ -453,7 +454,7 @@ class TensorflowGraphModel(Model):
		last_checkpoint)
		self._restored_model = True

		def predict(self, dataset, transformers=[], pad_batch=False):
		def predict(self, dataset, transformers=[]):
		"""
		Uses self to make predictions on provided Dataset object.

		@@ -467,7 +468,7 @@ class TensorflowGraphModel(Model):
		for (X_batch, _, _, ids_batch) in dataset.iterbatches(
		self.batch_size, deterministic=True):
		n_samples = len(X_batch)
		y_pred_batch = self.predict_on_batch(X_batch, pad_batch=pad_batch)
		y_pred_batch = self.predict_on_batch(X_batch)
		# Discard any padded predictions
		y_pred_batch = y_pred_batch[:n_samples]
		y_pred_batch = np.reshape(y_pred_batch, (n_samples, n_tasks))
		@@ -484,7 +485,7 @@ class TensorflowGraphModel(Model):
		y_pred = np.reshape(y_pred, (n_samples,))
		return y_pred

		def predict_proba(self, dataset, transformers=[], n_classes=2, pad_batch=False):
		def predict_proba(self, dataset, transformers=[], n_classes=2):
		"""
		TODO: Do transformers even make sense here?

		@@ -497,7 +498,7 @@ class TensorflowGraphModel(Model):
		for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(
		self.batch_size, deterministic=True):
		n_samples = len(X_batch)
		y_pred_batch = self.predict_proba_on_batch(X_batch, pad_batch=pad_batch)
		y_pred_batch = self.predict_proba_on_batch(X_batch)
		y_pred_batch = y_pred_batch[:n_samples]
		y_pred_batch = np.reshape(y_pred_batch, (n_samples, n_tasks, n_classes))
		y_pred_batch = undo_transforms(y_pred_batch, transformers)
		@@ -510,7 +511,7 @@ class TensorflowGraphModel(Model):
		y_pred = np.reshape(y_pred, (n_samples, n_tasks, n_classes))
		return y_pred

		def evaluate(self, dataset, metrics, transformers=[], pad_batch=False):
		def evaluate(self, dataset, metrics, transformers=[]):
		"""
		Evaluates the performance of this model on specified dataset.

		@@ -529,7 +530,7 @@ class TensorflowGraphModel(Model):
		Maps tasks to scores under metric.
		"""
		evaluator = Evaluator(self, dataset, transformers)
		scores = evaluator.compute_model_performance(metrics, pad_batch=pad_batch)
		scores = evaluator.compute_model_performance(metrics)
		return scores

		def _find_last_checkpoint(self):
		@@ -595,7 +596,7 @@ class TensorflowClassifier(TensorflowGraphModel):
		name='labels_%d' % task)))
		return labels

		def predict_on_batch(self, X, pad_batch=False):
		def predict_on_batch(self, X):
		"""Return model output for the provided input.

		Restore(checkpoint) must have previously been called on this object.
		@@ -616,7 +617,7 @@ class TensorflowClassifier(TensorflowGraphModel):
		ValueError: If output and labels are not both 3D or both 2D.
		"""
		len_unpadded = len(X)
		if pad_batch:
		if self.pad_batches:
		X = pad_features(self.batch_size, X)

		if not self._restored_model:
		@@ -651,7 +652,7 @@ class TensorflowClassifier(TensorflowGraphModel):
		outputs = outputs[:len_unpadded]
		return outputs

		def predict_proba_on_batch(self, X, pad_batch=False):
		def predict_proba_on_batch(self, X):
		"""Return model output for the provided input.

		Restore(checkpoint) must have previously been called on this object.
		@@ -669,7 +670,7 @@ class TensorflowClassifier(TensorflowGraphModel):
		AssertionError: If model is not in evaluation mode.
		ValueError: If output and labels are not both 3D or both 2D.
		"""
		if pad_batch:
		if self.pad_batches:
		X = pad_features(self.batch_size, X)
		if not self._restored_model:
		self.restore()
		@@ -744,7 +745,7 @@ class TensorflowRegressor(TensorflowGraphModel):
		name='labels_%d' % task)))
		return labels

		def predict_on_batch(self, X, pad_batch=False):
		def predict_on_batch(self, X):
		"""Return model output for the provided input.

		Restore(checkpoint) must have previously been called on this object.
		@@ -765,7 +766,7 @@ class TensorflowRegressor(TensorflowGraphModel):
		ValueError: If output and labels are not both 3D or both 2D.
		"""
		len_unpadded = len(X)
		if pad_batch:
		if self.pad_batches:
		X = pad_features(self.batch_size, X)

		if not self._restored_model:

deepchem/models/tensorflow_models/lr.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -157,8 +157,8 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		(self.batch_size,))
		return TensorflowGraph.get_feed_dict(orig_dict)

		def predict_proba_on_batch(self, X, pad_batch=False):
		if pad_batch:
		def predict_proba_on_batch(self, X):
		if self.pad_batches:
		X = pad_features(self.batch_size, X)
		if not self._restored_model:
		self.restore()
		@@ -190,9 +190,9 @@ class TensorflowLogisticRegression(TensorflowGraphModel):

		return np.copy(outputs)

		def predict_on_batch(self, X, pad_batch=False):
		def predict_on_batch(self, X):

		if pad_batch:
		if self.pad_batches:
		X = pad_features(self.batch_size, X)

		if not self._restored_model:

deepchem/models/tensorflow_models/progressive_joint.py

+5 −7

Original line number	Diff line number	Diff line
		@@ -199,8 +199,8 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		name="U_layer_%d_task%d" % (i, task), dtype=tf.float32)
		return tf.matmul(prev_layer, U)

		def fit(self, dataset, nb_epoch=10, pad_batches=False,
		max_checkpoints_to_keep=5, log_every_N_batches=50, **kwargs):
		def fit(self, dataset, nb_epoch=10, max_checkpoints_to_keep=5,
		log_every_N_batches=50, **kwargs):
		"""Fit the model.

		Parameters
		@@ -209,8 +209,6 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		Dataset object holding training data
		nb_epoch: 10
		Number of training epochs.
		pad_batches: bool
		Whether or not to pad each batch to exactly be of size batch_size.
		max_checkpoints_to_keep: int
		Maximum number of checkpoints to keep; older checkpoints will be deleted.
		log_every_N_batches: int
		@@ -240,7 +238,7 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		# Turns out there are valid cases where we don't want pad-batches
		# on by default.
		#dataset.iterbatches(batch_size, pad_batches=True)):
		dataset.iterbatches(self.batch_size, pad_batches=pad_batches)):
		dataset.iterbatches(self.batch_size, pad_batches=self.pad_batches)):
		if ind % log_every_N_batches == 0:
		log("On batch %d" % ind, self.verbose)
		# Run training op.
		@@ -344,7 +342,7 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		(self.batch_size,))
		return TensorflowGraph.get_feed_dict(orig_dict)

		def predict_on_batch(self, X, pad_batch=False):
		def predict_on_batch(self, X):
		"""Return model output for the provided input.

		Restore(checkpoint) must have previously been called on this object.
		@@ -365,7 +363,7 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		ValueError: If output and labels are not both 3D or both 2D.
		"""
		len_unpadded = len(X)
		if pad_batch:
		if self.pad_batches:
		X = pad_features(self.batch_size, X)

		if not self._restored_model:

deepchem/models/tensorflow_models/progressive_multitask.py

+2 −4

Original line number	Diff line number	Diff line
		@@ -523,7 +523,7 @@ class ProgressiveMultitaskRegressor(TensorflowMultiTaskRegressor):
		return self.eval_graph.session


		def fit_task(self, sess, dataset, task, task_train_op, nb_epoch=10, pad_batches=False,
		def fit_task(self, sess, dataset, task, task_train_op, nb_epoch=10,
		log_every_N_batches=50):
		"""Fit the model.

		@@ -540,8 +540,6 @@ class ProgressiveMultitaskRegressor(TensorflowMultiTaskRegressor):
		The index of the task to train on.
		nb_epoch: 10
		Number of training epochs.
		pad_batches: bool
		Whether or not to pad each batch to exactly be of size batch_size.
		max_checkpoints_to_keep: int
		Maximum number of checkpoints to keep; older checkpoints will be deleted.
		log_every_N_batches: int
		@@ -564,7 +562,7 @@ class ProgressiveMultitaskRegressor(TensorflowMultiTaskRegressor):
		# Turns out there are valid cases where we don't want pad-batches
		# on by default.
		#dataset.iterbatches(batch_size, pad_batches=True)):
		dataset.iterbatches(self.batch_size, pad_batches=pad_batches)):
		dataset.iterbatches(self.batch_size, pad_batches=self.pad_batches)):
		if ind % log_every_N_batches == 0:
		log("On batch %d" % ind, self.verbose)
		feed_dict = self.construct_task_feed_dict(task, X_b, y_b, w_b, ids_b)

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