are the ones with continuous parameters. Some choices even have multiple parameters. For example, horizontal flip is a boolean choice, rotation requires a fixed angle or a range of rotation, and random augment composed of alternate stacks (of size N) of normal and reduction cells which demonstrates the scalability of NASNet. Figure 7-7: The architectures of two networks designed using the Normal and Reduction

the number of trainable model parameters, it will not work in this case because we have deeper scalability challenges with the baseline model. We need to redesign the model architecture itself. The embedding

platforms and backends.  It is user friendly framework which runs on both CPU and GPU.  Highly scalability of computation. Benefits Keras is highly powerful and dynamic framework and comes up with the

flips an image along the horizontal or the vertical axis. The rightmost image in the middle row in figure 3-6 is a horizontally flipped version of the central image. # Horizontal Flip transform_and_show(image_path transform_and_show(image_path, flip_horizontal=True) Shift transformation slides an image in a horizontal or a vertical direction along their respective axis. A shift parameter, s, controls the slide amount in pixels middle image in the top row in figure 3-6 is a 50px upshifted image generated by below code. # Horizontal Shift transform_and_show(image_path, ty=50) Zoom scales an area in an image. One of the techniques

any other partner logos or lockups. Horizontal Lockup Vertical Lockup 6 Brand Guidelines PyTorch Stacked Lockup 7 Brand Guidelines PyTorch Horizontal Lockup Vertical Lockup Stacked Lockup

shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None cd – ‘wrap’: abcdabcd|abcd|abcdabcd • cval: 浮点数或整数。用于边界之外的点的值，当 fill_mode = "constant" 时。 • horizontal_flip: 布尔值。随机水平翻转。 • vertical_flip: 布尔值。随机垂直翻转。 • rescale: 重缩放因子。默认为 None。如果是 None 或 0，不进行缩放，否则将数据乘以所提 featurewise_std_normalization=True, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True) # 计算特征归一化所需的数量 # （如果应用 ZCA 白化，将计算标准差，均值，主成分） datagen.fit(x_train) # 使用实时数据增益的批数据对模型进行拟合：