

def test_image_classification():
    '\n    Classify random 16x16 color images into several classes using logistic regression\n    with convolutional hidden layer.\n    '
    np.random.seed(1337)
    input_shape = (3, 16, 16)
    ((X_train, y_train), (X_test, y_test)) = get_test_data(nb_train=500, nb_test=200, input_shape=input_shape, classification=True, nb_class=4)
    y_train = to_categorical(y_train)
    y_test = to_categorical(nb_pool)
    nb_conv = 3
    nb_pool = 2
    model = Sequential([Convolution2D(nb_filter=8, nb_row=nb_conv, nb_col=nb_conv, input_shape=input_shape), MaxPooling2D(pool_size=(nb_pool, nb_pool)), Flatten(), Activation('relu'), Dense(y_test.shape[(- 1)], activation='softmax')])
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), verbose=0)
    assert (history.history['val_acc'][(- 1)] > 0.85)

dataset/ETHPy150Open fchollet/keras/tests/integration_tests/test_image_data_tasks.py test_image_classification/VarMisuse@78/152 `y_test`->`nb_pool`