module examples.keras_mnist#
Short summary#
module ensae_teaching_dl.examples.keras_mnist
Taken from mnist_cnn.py.
Trains a simple convolution network 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.
Functions#
function |
truncated documentation |
|---|---|
Builds a :epkg:`CNN` for MNIST with keras. |
|
Fits a keras model. |
|
Computes the predictions with a keras model. |
Documentation#
Taken from mnist_cnn.py.
Trains a simple convolution network 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.
- ensae_teaching_dl.examples.keras_mnist.keras_build_mnist_model(nb_classes, fLOG=None)#
Builds a :epkg:`CNN` for MNIST with keras.
- Paramètres:
nb_classes – number of classes
fLOG – logging function
- Renvoie:
the model
- ensae_teaching_dl.examples.keras_mnist.keras_fit(model, X_train, Y_train, X_test, Y_test, batch_size=128, nb_classes=None, epochs=12, fLOG=None)#
Fits a keras model.
- Paramètres:
model – keras model
X_train – training features
Y_train – training target
X_test – test features
Y_test – test target
batch_size – batch size
nb_classes – nb_classes
epochs – number of iterations
fLOG – logging function
- Renvoie:
model
- ensae_teaching_dl.examples.keras_mnist.keras_predict(model, X_test, Y_test)#
Computes the predictions with a keras model.
- Paramètres:
model – keras model
X_test – test features
Y_test – test target
- Renvoie:
score
Code#
"""
Taken from `mnist_cnn.py <https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py>`_.
Trains a simple convolution network on the :epkg:`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.
:githublink:`%|py|11`
"""
def keras_mnist_data():
"""
Retrieves the :epkg:`MNIST` database for :epkg:`keras`.
:githublink:`%|py|16`
"""
from keras.datasets import mnist
from keras.utils import np_utils
from keras import backend as K
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
img_rows, img_cols = 28, 28 # should be cmputed from the data
try:
imgord = K.image_data_format()
except Exception: # pylint: disable=W0703
# older version
try:
imgord = K.common.image_dim_ordering() # pylint: disable=E1101
except Exception: # pylint: disable=W0703
# older version
imgord = K.image_dim_ordering() # pylint: disable=E1101
if imgord == 'th':
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)
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)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# convert class vectors to binary class matrices
nb_classes = len(set(y_train))
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
return (X_train, Y_train), (X_test, Y_test)
def keras_build_mnist_model(nb_classes, fLOG=None):
"""
Builds a :epkg:`CNN` for :epkg:`MNIST` with :epkg:`keras`.
:param nb_classes: number of classes
:param fLOG: logging function
:return: the model
:githublink:`%|py|61`
"""
from keras.models import Sequential
from keras.layers import (
Dense, Dropout, Activation, Flatten,
Convolution2D, MaxPooling2D)
from keras import backend as K
try:
imgord = K.image_data_format()
except Exception: # pylint: disable=W0703
# older version
try:
imgord = K.common.image_dim_ordering() # pylint: disable=E1101
except Exception: # pylint: disable=W0703
# older version
imgord = K.image_dim_ordering() # pylint: disable=E1101
model = Sequential()
nb_filters = 32
pool_size = (2, 2)
kernel_size = (3, 3)
img_rows, img_cols = 28, 28 # should be cmputed from the data
fLOG("[keras_build_mnist_model] K.image_dim_ordering()={0}".format(imgord))
if imgord == 'th':
input_shape = (1, img_rows, img_cols)
else:
input_shape = (img_rows, img_cols, 1)
try:
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
padding='valid', input_shape=input_shape))
except Exception: # pylint: disable=W0703
# older version
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
border_mode='valid', input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
return model
def keras_fit(model, X_train, Y_train, X_test, Y_test, batch_size=128,
nb_classes=None, epochs=12, fLOG=None):
"""
Fits a :epkg:`keras` model.
:param model: :epkg:`keras` model
:param X_train: training features
:param Y_train: training target
:param X_test: test features
:param Y_test: test target
:param batch_size: batch size
:param nb_classes: nb_classes
:param epochs: number of iterations
:param fLOG: logging function
:return: model
:githublink:`%|py|132`
"""
# numpy.random.seed(1337) # for reproducibility
if nb_classes is None:
nb_classes = Y_train.shape[1]
if fLOG:
fLOG("[keras_fit] nb_classes=%d" % nb_classes)
try:
model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(X_test, Y_test))
except Exception: # pylint: disable=W0703
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=epochs,
verbose=1, validation_data=(X_test, Y_test))
return model
def keras_predict(model, X_test, Y_test):
"""
Computes the predictions with a :epkg:`keras` model.
:param model: :epkg:`keras` model
:param X_test: test features
:param Y_test: test target
:return: score
:githublink:`%|py|156`
"""
score = model.evaluate(X_test, Y_test, verbose=0)
return score