decay_epochs = [10000, 20000] hidden1_units = 8 hidden2_units = 8 hidden3_units = 8 conv_patch_size = 3 keep_probs = [1.0, 1.0] model = All_CNN_C(input_side=input_side, input_channels=input_channels, conv_patch_size=conv_patch_size, hidden1_units=hidden1_units, hidden2_units=hidden2_units, hidden3_units=hidden3_units, weight_decay=weight_decay, num_classes=num_classes, batch_size=batch_size, data_sets=data_sets, initial_learning_rate=initial_learning_rate, damping=1e-2, decay_epochs=decay_epochs, mini_batch=True, train_dir='output', log_dir='log', model_name='mnist') num_steps = 5000 model.train(num_steps=num_steps, iter_to_switch_to_batch=100000, iter_to_switch_to_sgd=100000) iter_to_load = num_steps - 1
hidden1_units = 32 hidden2_units = 32 hidden3_units = 32 conv_patch_size = 3 keep_probs = [1.0, 1.0] model = All_CNN_C( input_side=input_side, input_channels=input_channels, conv_patch_size=conv_patch_size, hidden1_units=hidden1_units, hidden2_units=hidden2_units, hidden3_units=hidden3_units, weight_decay=weight_decay, num_classes=num_classes, batch_size=batch_size, data_sets=data_sets, initial_learning_rate=initial_learning_rate, damping=1e-1, # try changing? 80 decay_epochs=decay_epochs, mini_batch=True, train_dir='output', log_dir='log', model_name='fashion_mnist') num_steps = 5000 model.train( num_steps=num_steps, iter_to_switch_to_batch=1000000, iter_to_switch_to_sgd=100000)
def init_model(self): """ Initialize a tf model based on model_name and datasets """ # TODO: make it easier to use non-default hyperparams? # we can always infer # classes of from the training data num_classes = len(set(self.data_sets.train.labels)) model_name = self.task + '_' + self.model_name print('Num classes', num_classes) if self.model_name == 'binary_logistic': #num_classes = 2 assert num_classes == 2 weight_decay = 0.0001 batch_size = 100 initial_learning_rate = 0.001 keep_probs = None decay_epochs = [1000, 10000] max_lbfgs_iter = 1000 self.model = BinaryLogisticRegressionWithLBFGS( input_dim=self.input_dim, weight_decay=weight_decay, max_lbfgs_iter=max_lbfgs_iter, num_classes=num_classes, batch_size=batch_size, data_sets=self.data_sets, initial_learning_rate=initial_learning_rate, keep_probs=keep_probs, decay_epochs=decay_epochs, mini_batch=False, train_dir='output', log_dir='log', model_name=model_name ) elif self.model_name == 'multi_logistic': #num_classes = 10 weight_decay = 0.01 batch_size = 1400 initial_learning_rate = 0.001 keep_probs = None max_lbfgs_iter = 1000 decay_epochs = [1000, 10000] self.model = LogisticRegressionWithLBFGS( input_dim=self.input_dim, weight_decay=weight_decay, max_lbfgs_iter=max_lbfgs_iter, num_classes=num_classes, batch_size=batch_size, data_sets=self.data_sets, initial_learning_rate=initial_learning_rate, keep_probs=keep_probs, decay_epochs=decay_epochs, mini_batch=False, train_dir='output', log_dir='log', model_name=model_name) elif self.model_name == 'cnn': assert num_classes == 10 weight_decay = 0.001 batch_size = 500 initial_learning_rate = 0.0001 decay_epochs = [10000, 20000] hidden1_units = 8 hidden2_units = 8 hidden3_units = 8 conv_patch_size = 3 keep_probs = [1.0, 1.0] self.model = All_CNN_C( input_side=self.input_side, input_channels=self.input_channels, conv_patch_size=conv_patch_size, hidden1_units=hidden1_units, hidden2_units=hidden2_units, hidden3_units=hidden3_units, weight_decay=weight_decay, num_classes=num_classes, batch_size=batch_size, data_sets=self.data_sets, initial_learning_rate=initial_learning_rate, damping=1e-2, decay_epochs=decay_epochs, mini_batch=True, train_dir='output', log_dir='log', model_name=model_name ) elif self.task == 'income': num_classes = 2 input_dim = self.data_sets.train.x.shape[1] weight_decay = 0.0001 # weight_decay = 1000 / len(lr_data_sets.train.labels) batch_size = 10 initial_learning_rate = 0.001 keep_probs = None decay_epochs = [1000, 10000] max_lbfgs_iter = 1000 self.model = BinaryLogisticRegressionWithLBFGS( input_dim=input_dim, weight_decay=weight_decay, max_lbfgs_iter=max_lbfgs_iter, num_classes=num_classes, batch_size=batch_size, data_sets=self.data_sets, initial_learning_rate=initial_learning_rate, keep_probs=keep_probs, decay_epochs=decay_epochs, mini_batch=False, train_dir='output', log_dir='log', model_name='income_logreg' ) elif self.model_name == 'hinge_svm': #num_classes = 2 weight_decay = 0.01 use_bias = False batch_size = 100 initial_learning_rate = 0.001 keep_probs = None decay_epochs = [1000, 10000] temps = [0, 0.001, 0.1] num_temps = len(temps) num_params = 784 temp = 0 self.model = SmoothHinge( use_bias=use_bias, temp=temp, input_dim=self.input_dim, weight_decay=weight_decay, num_classes=num_classes, batch_size=batch_size, data_sets=self.data_sets, initial_learning_rate=initial_learning_rate, keep_probs=keep_probs, decay_epochs=decay_epochs, mini_batch=False, train_dir='output', log_dir='log', model_name='smooth_hinge_17_t-%s' % temp)