clearml/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py

# 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 tempfile
import os

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  # noqa: F401

from trains import Task, Logger


# Connecting TRAINS
task = Task.init(project_name='examples', task_name='Keras HP optimization base')


# 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)

args = {'batch_size': 128,
        'epochs': 6,
        'layer_1': 512,
        'layer_2': 512,
        'layer_3': 10,
        'layer_4': 512,
        }
args = task.connect(args)

model = Sequential()
model.add(Dense(args['layer_1'], input_shape=(784,)))
model.add(Activation('relu'))
# model.add(Dropout(0.2))
model.add(Dense(args['layer_2']))
model.add(Activation('relu'))
# model.add(Dropout(0.2))
model.add(Dense(args['layer_3']))
model.add(Activation('softmax'))

model2 = Sequential()
model2.add(Dense(args['layer_4'], input_shape=(784,)))
model2.add(Activation('relu'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

# 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(log_dir=output_folder, write_images=False)
model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.hdf5'))

history = model.fit(X_train, Y_train,
                    batch_size=args['batch_size'], epochs=args['epochs'],
                    callbacks=[board, model_store],
                    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])
Logger.current_logger().report_scalar(title='evaluate', series='score', value=score[0], iteration=args['epochs'])
Logger.current_logger().report_scalar(title='evaluate', series='accuracy', value=score[1], iteration=args['epochs'])