base_model = VGG19(weights=None, include_top=False, pooling='avg', input_shape=(LEFT, RIGHT, 3))

    # add a global spatial average pooling layer
    x = base_model.output
    x = Dense(1024, activation='relu')(x)

    # and a logistic layer -- let's say we have 2 classes
    predictions = Dense(2, activation='softmax')(x)

    # this is the model we will train
    model = Model(inputs=base_model.input, outputs=predictions)

    # Print the layers
    for i, layer in enumerate(model.layers):
        print(i, layer.name, layer.output_shape)
    plot_model(model, show_shapes=True, to_file=MODELDIR + IDENTNAME + '_model.png')

    # we chose to train the top  inception blocks, i.e. we will freeze
    # the first 5 layers and unfreeze the rest:
    for layer in model.layers[:10]:
        layer.trainable = True
    for layer in model.layers[10:]:
        layer.trainable = True

    # we need to recompile the model for these modifications to take effect

    from keras.optimizers import Adam

    optimizer = Adam(lr=0.00008, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=True)

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

    history = model.fit_generator(generator(BATCHSIZE, DATADIR), steps_per_epoch=DATASTEPS,
                                  validation_data=generator(NUMVALIDATIONFILES, VALIDATIONDIR), validation_steps=1,
                                  epochs=EPOCHS, verbose=1, class_weight={0: 1, 1: 1})

    # Save model and weights....
    # serialize model to YAML
    model_yaml = model.to_yaml()
    with open(MODELDIR + IDENTNAME + '_model.yaml', "w") as yaml_file:
        yaml_file.write(model_yaml)
    # serialize weights to HDF5
    model.save_weights(MODELDIR + IDENTNAME + '_weights.h5')
    print("Saved model to disk")

#######predict one image#####
   from keras.preprocessing.image import load_img
    image = load_img('picture.png', target_size=(64, 64))
    from keras.preprocessing.image import img_to_array
    image = img_to_array(image)
    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
    from keras.applications.vgg19 import preprocess_input
    image = preprocess_input(image)
    yhat = model.predict(image)

    # create a list containing the class labels
    class_labels = ['class1', 'class2']

    # find the index of the class with maximum score
    pred = np.argmax(class_labels, axis=-1)

    # print the label of the class with maximum score
    print(class_labels[pred[0]])

batch_features[i, :, :, :] = imageio.imread(t)[:, :, :3]

from keras.preprocessing.image import load_img
    image = load_img('picture.png', target_size=(64, 64, 3))
    np.expand_dims(image, axis=0)
    from keras.preprocessing.image import img_to_array
    image = img_to_array(image)
    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

    from keras.applications.vgg19 import preprocess_input
    image = preprocess_input(image)
    yhat = model.predict(image)
    # create a list containing the class labels
    class_labels = ['class1', 'class2']
    # find the index of the class with maximum score
    pred = np.argmax(class_labels, axis=-1)
    # print the label of the class with maximum score
    print(class_labels[pred[0]])

image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

?