Test script for CUDA/cuDNN

mnist_gpu_test_script.py

import numpy as np

np.random.seed(1337)
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop

NB_CLASSES = 10
BATCH_SIZE = 128
NB_EPOCH = 20


def transform_data(data, nb_classes):
    (X_train, y_train), (X_test, y_test) = data
    X_train = X_train.reshape(60000, 784)
    X_test = X_test.reshape(10000, 784)
    X_train = X_train.astype("float32")
    X_test = X_test.astype("float32")
    X_train /= 255
    X_test /= 255
    y_train = np_utils.to_categorical(y_train, nb_classes)
    y_test = np_utils.to_categorical(y_test, nb_classes)
    return X_train, X_test, y_train, y_test


def evaluate_model(X_train, X_test, y_train, y_test, batch_size, nb_epoch):
    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"))
    model.compile(loss="categorical_crossentropy",
                  optimizer=RMSprop(),
                  metrics=["accuracy"])
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              verbose=1, validation_data=(X_test, y_test))
    results = model.evaluate(X_test, y_test, verbose=0)
    return results


if __name__ == "__main__":
    X_train, X_test, y_train, y_test = transform_data(mnist.load_data(),
                                                      NB_CLASSES)

    results = evaluate_model(X_train, X_test, y_train, y_test,
                             BATCH_SIZE, NB_EPOCH)

    print(results)