<filename>sherpa-seq-testing/examples/parallel-examples/mnistcnnpbt/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
CONFIG = tf.ConfigProto(device_count = {'GPU': 1}, log_device_placement=False, allow_soft_placement=False)
CONFIG.gpu_options.allow_growth = True # Prevents tf from grabbing all gpu memory.
sess = tf.Session(config=CONFIG)
from keras import backend as K
K.set_session(sess)


import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.models import load_model
import numpy as np


################## Sherpa trial ##################
import sherpa
client = sherpa.Client()
trial = client.get_trial()  # contains ID and parameters
##################################################

batch_size = trial.parameters['batch_size']
num_classes = 10
epochs = trial.parameters['epochs']

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_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
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

if trial.parameters.get('load_from', '') == '':
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(trial.parameters.get('dropout', 0.25)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(2*trial.parameters.get('dropout', 0.25)))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.SGD(lr=trial.parameters['lr'],
                                                 momentum=trial.parameters.get('momentum', 0.7),
                                                 decay=trial.parameters.get('lr_decay', 0.)),
                  metrics=['accuracy'])
else:
    model = load_model(os.path.join('./output', trial.parameters['load_from']))
    K.set_value(model.optimizer.lr, trial.parameters['lr'])
    assert np.isclose(K.get_value(model.optimizer.lr), trial.parameters['lr'])
    K.set_value(model.optimizer.momentum, trial.parameters['momentum'])
    assert np.isclose(K.get_value(model.optimizer.momentum), trial.parameters['momentum'])


model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=[client.keras_send_metrics(trial, objective_name='val_loss', context_names=['val_acc'])],
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(os.path.join('./output', trial.parameters['save_to']))
<filename>sherpa-seq-testing/examples/parallel-examples/mnistcnnpbt/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
CONFIG = tf.ConfigProto(device_count = {'GPU': 1}, log_device_placement=False, allow_soft_placement=False)
CONFIG.gpu_options.allow_growth = True # Prevents tf from grabbing all gpu memory.
sess = tf.Session(config=CONFIG)
from keras import backend as K
K.set_session(sess)


import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.models import load_model
import numpy as np


################## Sherpa trial ##################
import sherpa
client = sherpa.Client()
trial = client.get_trial()  # contains ID and parameters
##################################################

batch_size = trial.parameters['batch_size']
num_classes = 10
epochs = trial.parameters['epochs']

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_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
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

if trial.parameters.get('load_from', '') == '':
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(trial.parameters.get('dropout', 0.25)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(2*trial.parameters.get('dropout', 0.25)))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.SGD(lr=trial.parameters['lr'],
                                                 momentum=trial.parameters.get('momentum', 0.7),
                                                 decay=trial.parameters.get('lr_decay', 0.)),
                  metrics=['accuracy'])
else:
    model = load_model(os.path.join('./output', trial.parameters['load_from']))
    K.set_value(model.optimizer.lr, trial.parameters['lr'])
    assert np.isclose(K.get_value(model.optimizer.lr), trial.parameters['lr'])
    K.set_value(model.optimizer.momentum, trial.parameters['momentum'])
    assert np.isclose(K.get_value(model.optimizer.momentum), trial.parameters['momentum'])


model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=[client.keras_send_metrics(trial, objective_name='val_loss', context_names=['val_acc'])],
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(os.path.join('./output', trial.parameters['save_to']))
Trains a simple convnet on the MNIST dataset. Gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. # Prevents tf from grabbing all gpu memory. ################## Sherpa trial ################## # contains ID and parameters ################################################## # input image dimensions # the data, shuffled and split between train and test sets # convert class vectors to binary class matrices