# TRAINS - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs # # Train a simple deep NN on the MNIST dataset. # Gets to 98.40% test accuracy after 20 epochs # (there is *a lot* of margin for parameter tuning). # 2 seconds per epoch on a K520 GPU. from __future__ import print_function import argparse import tempfile import os import numpy as np from keras.callbacks import TensorBoard, ModelCheckpoint from keras.datasets import mnist from keras.models import Sequential from keras.layers.core import Dense, Activation from keras.optimizers import RMSprop from keras.utils import np_utils import tensorflow as tf from trains import Task class TensorBoardImage(TensorBoard): @staticmethod def make_image(tensor): from PIL import Image import io tensor = np.stack((tensor, tensor, tensor), axis=2) height, width, channels = tensor.shape image = Image.fromarray(tensor) output = io.BytesIO() image.save(output, format='PNG') image_string = output.getvalue() output.close() return tf.Summary.Image(height=height, width=width, colorspace=channels, encoded_image_string=image_string) def on_epoch_end(self, epoch, logs=None): if logs is None: logs = {} super(TensorBoardImage, self).on_epoch_end(epoch, logs) images = self.validation_data[0] # 0 - data; 1 - labels img = (255 * images[0].reshape(28, 28)).astype('uint8') image = self.make_image(img) summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)]) self.writer.add_summary(summary, epoch) parser = argparse.ArgumentParser(description='Keras MNIST Example') parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training (default: 64)') parser.add_argument('--epochs', type=int, default=6, help='number of epochs to train (default: 10)') args = parser.parse_args() # the data, shuffled and split between train and test sets nb_classes = 10 (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(60000, 784).astype('float32')/255. X_test = X_test.reshape(10000, 784).astype('float32')/255. print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') # convert class vectors to binary class matrices Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) model = Sequential() model.add(Dense(512, input_shape=(784,))) model.add(Activation('relu')) # model.add(Dropout(0.2)) model.add(Dense(512)) model.add(Activation('relu')) # model.add(Dropout(0.2)) model.add(Dense(10)) model.add(Activation('softmax')) model2 = Sequential() model2.add(Dense(512, input_shape=(784,))) model2.add(Activation('relu')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy']) # Connecting TRAINS task = Task.init(project_name='examples', task_name='Keras with TensorBoard example') # Advanced: setting model class enumeration labels = dict(('digit_%d' % i, i) for i in range(10)) task.set_model_label_enumeration(labels) output_folder = os.path.join(tempfile.gettempdir(), 'keras_example') board = TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False) model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5')) # load previous model, if it is there try: model.load_weights(os.path.join(output_folder, 'weight.1.hdf5')) except: pass history = model.fit(X_train, Y_train, batch_size=args.batch_size, epochs=args.epochs, callbacks=[board, model_store], verbose=1, validation_data=(X_test, Y_test)) score = model.evaluate(X_test, Y_test, verbose=0) print('Test score:', score[0]) print('Test accuracy:', score[1])