def _setup_training(self): """Sets up graph, model and trainer.""" # Create config if not given. if self._config is None: self._config = RunConfig(verbose=self.verbose) # Create new graph. self._graph = ops.Graph() self._graph.add_to_collection("IS_TRAINING", True) with self._graph.as_default(): random_seed.set_random_seed(self._config.tf_random_seed) self._global_step = variables.Variable( 0, name="global_step", trainable=False) # Setting up inputs and outputs. self._inp, self._out = self._data_feeder.input_builder() # If class weights are provided, add them to the graph. # Different loss functions can use this tensor by name. if self.class_weight: self._class_weight_node = constant_op.constant( self.class_weight, name='class_weight') # Add histograms for X and y if they are floats. if self._data_feeder.input_dtype in (np.float32, np.float64): logging_ops.histogram_summary("X", self._inp) if self._data_feeder.output_dtype in (np.float32, np.float64): logging_ops.histogram_summary("y", self._out) # Create model's graph. self._model_predictions, self._model_loss = self.model_fn( self._inp, self._out) # Create trainer and augment graph with gradients and optimizer. # Additionally creates initialization ops. learning_rate = self.learning_rate optimizer = self.optimizer if callable(learning_rate): learning_rate = learning_rate(self._global_step) if callable(optimizer): optimizer = optimizer(learning_rate) self._train = optimizers.optimize_loss(self._model_loss, self._global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=self.clip_gradients) # Update ops during training, e.g. batch_norm_ops self._train = control_flow_ops.group(self._train, *ops.get_collection('update_ops')) # Merge all summaries into single tensor. self._summaries = logging_ops.merge_all_summaries() # Get all initializers for all trainable variables. self._initializers = variables.initialize_all_variables() # Create model's saver capturing all the nodes created up until now. self._saver = train.Saver( max_to_keep=self._config.keep_checkpoint_max, keep_checkpoint_every_n_hours=self._config.keep_checkpoint_every_n_hours) # Enable monitor to create validation data dict with appropriate tf placeholders self._monitor.create_val_feed_dict(self._inp, self._out) # Create session to run model with. self._session = session.Session(self._config.tf_master, config=self._config.tf_config) # Run parameter initializers. self._session.run(self._initializers)
def _setup_training(self): """Sets up graph, model and trainer.""" # Create config if not given. if self._config is None: self._config = RunConfig(verbose=self.verbose) # Create new graph. self._graph = ops.Graph() self._graph.add_to_collection("IS_TRAINING", True) with self._graph.as_default(): random_seed.set_random_seed(self._config.tf_random_seed) self._global_step = variables.Variable(0, name="global_step", trainable=False) # Setting up inputs and outputs. self._inp, self._out = self._data_feeder.input_builder() # If class weights are provided, add them to the graph. # Different loss functions can use this tensor by name. if self.class_weight: self._class_weight_node = constant_op.constant( self.class_weight, name='class_weight') # Add histograms for X and y if they are floats. if self._data_feeder.input_dtype in (np.float32, np.float64): logging_ops.histogram_summary("X", self._inp) if self._data_feeder.output_dtype in (np.float32, np.float64): logging_ops.histogram_summary("y", self._out) # Create model's graph. self._model_predictions, self._model_loss = self.model_fn( self._inp, self._out) # Set up a single operator to merge all the summaries self._summaries = logging_ops.merge_all_summaries() # Create trainer and augment graph with gradients and optimizer. # Additionally creates initialization ops. learning_rate = self.learning_rate optimizer = self.optimizer if callable(learning_rate): learning_rate = learning_rate(self._global_step) if callable(optimizer): optimizer = optimizer(learning_rate) self._train = optimizers.optimize_loss( self._model_loss, self._global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=self.clip_gradients) # Update ops during training, e.g. batch_norm_ops self._train = control_flow_ops.group( self._train, *ops.get_collection('update_ops')) # Get all initializers for all trainable variables. self._initializers = variables.initialize_all_variables() # Create model's saver capturing all the nodes created up until now. self._saver = train.Saver( max_to_keep=self._config.keep_checkpoint_max, keep_checkpoint_every_n_hours=self._config. keep_checkpoint_every_n_hours) # Enable monitor to create validation data dict with appropriate tf placeholders self._monitor.create_val_feed_dict(self._inp, self._out) # Create session to run model with. self._session = session.Session(self._config.tf_master, config=self._config.tf_config) # Run parameter initializers. self._session.run(self._initializers)
g = tf.Graph() # Data feeder converts to one-hot, and handles minibatching. #data_feeder = setup_train_data_feeder(X_train, y_train, n_classes, batch_size) with g.as_default(): seed = 42 random_seed.set_random_seed(seed) #inp, out = data_feeder.input_builder() inp = tf.placeholder(tf.float32, [None, n_features]) out = tf.placeholder(tf.float32, [None, n_classes]) global_step = tf.Variable(0, name="global_step", trainable=False) model_predictions, model_loss = model_fn(inp, out) #train_op = tf.train.AdagradOptimizer(lr, clip_gradients=5).minimize(model_loss) train_op = optimizers.optimize_loss(model_loss, global_step, learning_rate=lr, optimizer="Adagrad", clip_gradients=5) init_op = tf.initialize_all_variables() sess = tf.Session(graph=g) # Init sess.run(init_op) # Train train_loss_trace = [] #dict_feed_fn = data_feeder.get_feed_dict_fn() for i in range(n_epochs + 1): #feed_dict = dict_feed_fn() # {'input:0': X, 'output:0': Y }