January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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TFLearn core layers
TFLearn offers the following layers in the tflearn.layers.core module:
| Layer class | Description |
| input_data | This layer is used to specify the input layer for the neural network. |
| fully_connected | This layer is used to specify a layer where all the neurons are connected to all the neurons in the previous layer. |
| dropout | This layer is used to specify the dropout regularization. The input elements are scaled by 1/keep_prob while keeping the expected sum unchanged. |
| custom_layer | This layer is used to specify a custom function to be applied to the input. This class wraps our custom function and presents the function as a layer. |
| reshape | This layer reshapes the input into the output of specified shape. |
| flatten | This ... |
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