如何打印 Tensorflow V2 keras 模型的所有激活形状(比 summary() 更详细)? [英] How to print all activation shapes (more detailed than summary()) for Tensorflow V2 keras model?

问题描述

我在 Tensorflow v.0 和 v.1 上花了很多时间，现在我正在尝试 Tensorflow v.2 keras 模型.model.summary() 看起来简单方便，但缺乏细节.

I've spent a lot of time with Tensorflow v.0 and v.1, and now I'm trying Tensorflow v.2 keras model. model.summary() looked easy and convenient, but lack details.

这是一个玩具示例.假设我定义了如下自定义层和模型(函数式 API 样式和子类样式).

Here's a toy example. Let's say I define custom layers and models as below (a functional API style and a subclass syle).

请看下文.我想在自定义层中看到原始层，但 .summary() 只显示浅层信息(仅直接子层).

Please see below. I wanted to see primitive layers inside the custom layers, but .summary() only shows shallow information (only direct children layers).

玩具自定义层(层只是玩具定义):

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import (Dense, Conv2D, BatchNormalization)

class LayerA(tf.keras.layers.Layer):
    def __init__(self, num_outputs, **kwargs):
        super(LayerA, self).__init__(**kwargs)
        self.num_outputs = num_outputs
        self.dense1 = Dense(64)
        self.dense2 = Dense(128)
        self.dense3 = Dense(num_outputs)
        self.bn = BatchNormalization(trainable=True)

    def call(self, inputs, training=True):
        x = self.dense1(inputs)
        x = self.bn(x, training=training)
        x = self.dense1(inputs)
        x = self.bn(x, training=training)
        x = self.dense1(inputs)
        x = self.bn(x, training=training)
        return x
    
class LayerB(tf.keras.layers.Layer):
    def __init__(self, num_outputs, **kwargs):
        super(LayerB, self).__init__(**kwargs)
        self.num_outputs = num_outputs
        self.dense = Dense(64)
        self.bn = BatchNormalization(trainable=True)

    def call(self, inputs, training=True):
        x = self.dense(inputs)
        x = self.bn(x, training=training)
        return x

使用函数式 API 的模型定义:

inputs = tf.keras.Input(shape=(28), name='input')
x = LayerA(7, name='layer_a')(inputs)
x = LayerB(13, name='layer_b')(x)
x = tf.reduce_max(x, 1)
model_func = keras.Model(inputs=inputs, outputs=x, name='model')
model_func.summary()

# Results:
# Layer (type)                 Output Shape              # Param #   
# =================================================================
# input (InputLayer)           [(None, 28)]              0         
# _________________________________________________________________
# layer_a (LayerA)             (None, 64)                2112      
# layer_b (LayerB)             (None, 64)                4416      
# tf_op_layer_Max_1 (TensorFlo [(None,)]                 0         
# =================================================================
# Total params: 6,528
# Trainable params: 6,272
# Non-trainable params: 256

模型定义子类化:

class ModelA(tf.keras.Model):

    def __init__(self):
        super(ModelA, self).__init__()
        self.block_1 = LayerA(7, name='layer_a')
        self.block_2 = LayerB(13, name='layer_b')

    def call(self, inputs):
        x = self.block_1(inputs)
        x = self.block_2(x)
        x = tf.reduce_max(x, 1)
        return x

model_subclass = ModelA()
y = model_subclass(inputs)
model_subclass.summary()

### Result:
# Layer (type)                 Output Shape              Param #   
# =================================================================
# layer_a (LayerA)             (None, 64)                2112      
# layer_b (LayerB)             (None, 64)                4416      
# =================================================================
# Total params: 6,528
# Trainable params: 6,016
# Non-trainable params: 512

如何打印模型中 Conv 和 Dense 层的所有激活形状?例如，

How can I prints all the activation shapes of Conv and Dense layers in the model? For example,

layer_a/dense_1                          (None, ...)
layer_a/dense_2                          (None, ...)
layer_b/dense_1                          (None, ...)
layer_b/maybe-even-deeper-layer/conv2d_1 (None, ...)
... etc ...

在 Tensorflow v.0 或 v.1 中，我会这样做:

In Tensorflow v.0 or v.1, I would do something like:

for n in tf.get_default_graph().as_graph_def().node:
    print(n.name, n.shape)

当我有 keras 模型时，有没有办法打印更多细节?

Is there a way to prints more details when I have a keras model?

推荐答案

摘要不会自动执行此操作，因此您必须进行调整.例如，您可以创建一个循环摘要:

The summary will not do this automatically, so you have to adapt. You can, for instance, create a recurrent summary:

def full_summary(layer):

    #check if this layer has layers
    if hasattr(layer, 'layers'):
        print('summary for ' + layer.name)
        layer.summary()
        print('\n\n')

        for l in layer.layers:
            full_summary(l)

将其用作:

full_summary(my_model)
         

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