def main(train_files,
eval_files,
job_dir,
epochs,
batch_size,
num_classes):
"""Main program"""
(x_train, y_train), (x_test, y_test) = utils.load_data(train_files, eval_files)
if K.image_data_format() == 'channes_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
mnist_model = model.build_model(input_shape, num_classes)
mnist_model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# Convert the Keras model to TensorFlow SavedModel
model.to_savedmodel(mnist_model, os.path.join(job_dir, 'export'))
def dispatch(train_files, eval_files, job_dir, train_steps, eval_steps,
train_batch_size, eval_batch_size, learning_rate, eval_frequency,
first_layer_size, num_layers, scale_factor, eval_num_epochs,
num_epochs, checkpoint_epochs):
census_model = model.model_fn(INPUT_SIZE, CLASS_SIZE)
try:
os.makedirs(job_dir)
except:
pass
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
checkpoint_path = FILE_PATH
if not job_dir.startswith("gs://"):
checkpoint_path = os.path.join(job_dir, checkpoint_path)
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_loss',
verbose=1,
period=checkpoint_epochs,
mode='max')
# Continuous eval callback
evaluation = ContinuousEval(eval_frequency,
eval_files,
learning_rate,
job_dir,
steps=train_steps)
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
callbacks = [checkpoint, evaluation, tblog]
census_model.fit_generator(model.generator_input(train_files,
chunk_size=CHUNK_SIZE),
steps_per_epoch=train_steps,
epochs=num_epochs,
callbacks=callbacks)
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
if job_dir.startswith("gs://"):
census_model.save(CENSUS_MODEL)
copy_file_to_gcs(job_dir, CENSUS_MODEL)
else:
census_model.save(os.path.join(job_dir, CENSUS_MODEL))
# Convert the Keras model to TensorFlow SavedModel
model.to_savedmodel(census_model, os.path.join(job_dir, 'export'))
def dispatch(train_files, eval_files, job_dir, train_steps, eval_steps,
train_batch_size, eval_batch_size, learning_rate, eval_frequency,
first_layer_size, num_layers, scale_factor, eval_num_epochs,
num_epochs, checkpoint_epochs):
census_model = model.model_fn(INPUT_SIZE, CLASS_SIZE)
try:
os.makedirs(job_dir)
except:
pass
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(os.path.join(
job_dir, FILE_PATH),
monitor='val_loss',
verbose=1,
period=checkpoint_epochs,
mode='max')
# Continuous eval callback
evaluation = ContinuousEval(eval_frequency, eval_files, learning_rate,
job_dir)
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
#TODO: This needs to be fixed in h5py so that writes to GCS are possible
# Don't attempt to create checkpoints on Cloud ML Engine for now because
# h5py doesn't come with native GCS write capability
if job_dir.startswith('gs://'):
callbacks = [evaluation, tblog]
else:
callbacks = [checkpoint, evaluation, tblog]
census_model.fit_generator(model.generator_input(train_files,
chunk_size=CHUNK_SIZE),
steps_per_epoch=train_steps,
epochs=num_epochs,
callbacks=callbacks)
census_model.save(os.path.join(job_dir, CENSUS_MODEL))
# Convert the Keras model to TensorFlow SavedModel
model.to_savedmodel(census_model, os.path.join(job_dir, 'export'))
def dispatch(data_file, job_dir, num_epochs):
timestamp = str(time.time())
job_dir = job_dir + "/run" + timestamp
x_train, y_train, maxlen, nb_chars, char_indices, indices_char = model.get_training_data(
data_file[0])
embedding_dims = 128
filters = 250
kernel_size = 3
hidden_dims = 8
batch_size = 32
dropout_rate = 0.3
epochs = num_epochs
sentiment_model = model.model_fn(nb_chars=nb_chars,
maxlen=maxlen,
embedding_dims=embedding_dims,
hidden_dims=hidden_dims,
dropout_rate=dropout_rate,
filters=filters,
kernel_size=kernel_size)
try:
os.makedirs(job_dir)
except:
pass
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
checkpoint_path = FILE_PATH
if not job_dir.startswith("gs://"):
checkpoint_path = os.path.join(job_dir, checkpoint_path)
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
timestamp = str(time.time())
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(job_dir, 'logs'),
write_graph=True,
embeddings_freq=0)
callbacks = [checkpoint, tblog]
sentiment_model = model.compile_model(sentiment_model)
sentiment_model.fit(x_train,
y_train,
epochs=epochs,
validation_split=0.3,
batch_size=batch_size,
callbacks=callbacks)
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
if job_dir.startswith("gs://"):
sentiment_model.save(SENTIMENT_MODEL)
copy_file_to_gcs(job_dir, SENTIMENT_MODEL)
else:
sentiment_model.save(os.path.join(job_dir, SENTIMENT_MODEL))
# Convert the Keras model to TensorFlow SavedModel
model.to_savedmodel(sentiment_model, os.path.join(job_dir, 'export'))