x = tfg.Placeholder(name="x") target = tfg.Placeholder(name="target") n = tfn.Multi_Layer_Network( input_size=input_size, hidden_size_list=[hidden_layer1_size, hidden_layer2_size], output_size=output_size, input_node=x, target_node=target, init_mean=0.0, init_sd=0.01, activator=tfe.Activator.ReLU.value, optimizer=tfe.Optimizer.Adam.value, learning_rate=0.01, model_params_dir=model_params_dir ) #n.draw_and_show() data = mnist.MNIST_Data() forward_final_output = n.feed_forward(input_data=data.test_input, is_numba=False) print(forward_final_output.shape) print(tff.accuracy(forward_final_output, data.test_target)) batch_size = 1000 n.learning(max_epoch=100, data=data, batch_size=batch_size, print_period=1, is_numba=False, verbose=False) forward_final_output = n.feed_forward(input_data=data.test_input, is_numba=False) print(tff.accuracy(forward_final_output, data.test_target))
target = tfg.Placeholder(name="target") n = tfn.CNN( input_dim=input_dim, cnn_param_list=cnn_param_list, fc_hidden_size=fc_hidden_size, output_size=output_size, input_node=x, target_node=target, conv_initializer=tfe.Initializer.Conv_Xavier_Normal.value, initializer=tfe.Initializer.Normal.value, init_sd=0.01, # initializer=tfe.Initializer.Xavier.value, activator=tfe.Activator.ReLU.value, optimizer=tfe.Optimizer.Adam.value, learning_rate=0.001 ) #n.draw_and_show() data = mnist.MNIST_Data(validation_size=5000, n_splits=12, is_onehot_target=True, cnn=True) forward_final_output = n.feed_forward(input_data=data.test_input, is_numba=False) print(forward_final_output.shape) print(tff.accuracy(forward_final_output, data.test_target)) batch_size = 1000 n.learning(max_epoch=5, data=data, batch_size=batch_size, print_period=1, is_numba=False, verbose=False) forward_final_output = n.feed_forward(input_data=data.test_input, is_numba=False) print(tff.accuracy(forward_final_output, data.test_target))