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This article mainly shows you "what are the high-level architecture patterns of Python", which is easy to understand and well-organized. I hope it can help you solve your doubts. Let the editor lead you to study and learn what are the advanced architecture patterns of Python.

1. Residual connection is a commonly used component at present, which solves the problem of gradient disappearance and bottleneck of large-scale deep learning model.

In general, it may be helpful to append residual connections to models with more than 10 layers.

From keras import layers x =... Y = layers.Conv2D (128,3, activation='relu', padding='same') (x) y = layers.Conv2D (128,3, activation='relu', padding='same') (y) y = layers.MaxPooling2D (2, strides=2) (y) # to make a linear transformation: residual = layers.Conv2D (128,1, strides=2, padding='same') (x) # using 1 × 1 convolution, x is linearly downsampled to the same shape as y y = layers.add ([y, residual])

2. Standardization is used to make the different samples seen by the model more similar, which is helpful to the optimization and generalization of the model.

# Convconv_model.add (layers.Conv2D (32,3, activation='relu') conv_model.add (layers.BatchNormalization ()) # Densedense_model.add (layers.Dense (32, activation='relu')) dense_model.add (layers.BatchNormalization ()) 3. The deep separable convolution layer, called SeparableConv2D in Keras, has the same function as ordinary Conv2D. But SeparableConv2D is lighter, faster and more accurate than Conv2D. From tensorflow.keras.models import Sequential, Modelfrom tensorflow.keras import layers height = 64width = 64channels = 3num_classes = 10 model = Sequential () model.add (layers.SeparableConv2D (32, 3, activation='relu', input_shape= (height, width, channels,)) model.add (layers.SeparableConv2D (64, 3, activation='relu') model.add (layers.MaxPooling2D (2)) model.add (layers.SeparableConv2D (64) 3, activation='relu')) model.add (layers.SeparableConv2D (128,3, activation='relu')) model.add (layers.MaxPooling2D (2)) model.add (layers.SeparableConv2D (64,3, activation='relu')) model.add (layers.SeparableConv2D (128,3, activation='relu') model.add (layers.GlobalAveragePooling2D ()) model.add (layers.Dense (32, activation='relu') model.add (layers.Dense (num_classes, activation='softmax')) model.compile (optimizer='rmsprop') Loss='categorical_crossentropy') these are all the contents of the article "what are the Advanced Architecture patterns of Python?" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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