Keras Example

https://github.com/ModelChimp/keras_example

from __future__ import print_function

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

# MODELCHIMP
from modelchimp import Tracker
from modelchimp.keras import ModelChimpCallback

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train[:2000]
y_train = y_train[:2000]
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)

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(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))


param = {
    'loss' : keras.losses.categorical_crossentropy,
    'optimizer' : keras.optimizers.Adadelta(),
    'batch_size' : batch_size,
    'epochs' : epochs
}

# MODELCHIMP Tracker instantiation
tracker = Tracker('<PROJECT KEY>', host='localhost:8000', experiment_name='MNIST Classification')
tracker.add_multiple_params(param)

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

model.fit(x_train, y_train,
          batch_size=param['batch_size'],
          epochs=param['batch_size'],
          verbose=1,
          validation_data=(x_test, y_test),
          callbacks=[ModelChimpCallback(),]) # MODELCHIMP Callback