mirror of
https://github.com/clearml/clearml
synced 2025-01-31 17:17:00 +00:00
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
# 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: 128)')
|
|
parser.add_argument('--epochs', type=int, default=6, help='number of epochs to train (default: 6)')
|
|
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')
|
|
task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
|
|
|
|
# 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])
|