model.summary() 的输出不是预期的 tensorflow 2 [英] output of model.summary() is not as expected tensorflow 2
问题描述
我已经定义了一个复杂的深度学习模型，但就这个问题而言，我将使用一个简单的模型.
I've defined a complex deep learning model, but for the purpose of this question, I'll use a simple one.
考虑以下事项:
import tensorflow as tf
from tensorflow.keras import layers, models
def simpleMLP(in_size, hidden_sizes, num_classes, dropout_prob=0.5):
in_x = layers.Input(shape=(in_size,))
hidden_x = models.Sequential(name="hidden_layers")
for i, num_h in enumerate(hidden_sizes):
hidden_x.add(layers.Dense(num_h, input_shape=(in_size,) if i == 0 else []))
hidden_x.add(layers.Activation('relu'))
hidden_x.add(layers.Dropout(dropout_prob))
out_x = layers.Dense(num_classes, activation='softmax', name='baseline')
return models.Model(inputs=in_x, outputs=out_x(hidden_x(in_x)))
我将通过以下方式调用该函数:
I will call the function in the following manner:
mdl = simpleMLP(28*28, [500, 300], 10)
现在，当我执行 mdl.summary() 时，我得到以下信息:
Now when I do mdl.summary() I get the following:
Model: "functional_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 784)] 0
_________________________________________________________________
hidden_layers (Sequential) (None, 300) 542800
_________________________________________________________________
baseline (Dense) (None, 10) 3010
=================================================================
Total params: 545,810
Trainable params: 545,810
Non-trainable params: 0
_________________________________________________________________
问题是Sequential块被压缩，只显示最后一层，但显示参数的总和.
The problem is that the Sequential block is condensed and showing only the last layer but the sum total of parameters.
在我的复杂模型中，我有多个 Sequential 块都被隐藏了.
In my complex model, I have multiple Sequential blocks that are all hidden.
有没有办法让它更冗长?我在模型定义中做错了什么吗?
Is there a way to make it be more verbose? Am I doing something wrong in the model definition?
当使用 pytorch 时，我没有看到相同的行为，给出以下示例(取自 这里):
When using pytorch I don't see the same behaviour, given the following example (taken from here):
import torch
import torch.nn as nn
class MyCNNClassifier(nn.Module):
def __init__(self, in_c, n_classes):
super().__init__()
self.conv_block1 = nn.Sequential(
nn.Conv2d(in_c, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU()
)
self.conv_block2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(32 * 28 * 28, 1024),
nn.Sigmoid(),
nn.Linear(1024, n_classes)
)
def forward(self, x):
x = self.conv_block1(x)
x = self.conv_block2(x)
x = x.view(x.size(0), -1) # flat
x = self.decoder(x)
return x
打印时我得到:
MyCNNClassifier(
(conv_block1): Sequential(
(0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv_block2): Sequential(
(0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(decoder): Sequential(
(0): Linear(in_features=25088, out_features=1024, bias=True)
(1): Sigmoid()
(2): Linear(in_features=1024, out_features=10, bias=True)
)
)
推荐答案
Tensorflow 2.x 中的模型摘要没有任何问题.
There is nothing wrong in model summary in Tensorflow 2.x.
import tensorflow as tf
from tensorflow.keras import layers, models
def simpleMLP(in_size, hidden_sizes, num_classes, dropout_prob=0.5):
in_x = layers.Input(shape=(in_size,))
hidden_x = models.Sequential(name="hidden_layers")
for i, num_h in enumerate(hidden_sizes):
hidden_x.add(layers.Dense(num_h, input_shape=(in_size,) if i == 0 else []))
hidden_x.add(layers.Activation('relu'))
hidden_x.add(layers.Dropout(dropout_prob))
out_x = layers.Dense(num_classes, activation='softmax', name='baseline')
return models.Model(inputs=in_x, outputs=out_x(hidden_x(in_x)))
mdl = simpleMLP(28*28, [500, 300], 10)
mdl.summary()
输出:
Model: "functional_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 784)] 0
_________________________________________________________________
hidden_layers (Sequential) (None, 300) 542800
_________________________________________________________________
baseline (Dense) (None, 10) 3010
=================================================================
Total params: 545,810
Trainable params: 545,810
Non-trainable params: 0
_________________________________________________________________
您可以使用 get_layer 来检索图层它的名称或索引.
You can use get_layer to retrieve a layer on either its name or index.
如果同时提供了名称和索引，则索引优先.索引基于水平图遍历的顺序(自下而上).
If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).
这里获取Sequential层(即在mdl中索引为1)的详细信息，您可以尝试
Here to get Sequential layer (i.e. indexed at 1 in mdl) details, you can try
mdl.get_layer(index=1).summary()
输出:
Model: "hidden_layers"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 500) 392500
_________________________________________________________________
activation_2 (Activation) (None, 500) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 500) 0
_________________________________________________________________
dense_3 (Dense) (None, 300) 150300
_________________________________________________________________
activation_3 (Activation) (None, 300) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 300) 0
=================================================================
Total params: 542,800
Trainable params: 542,800
Non-trainable params: 0
_________________________________________________________________
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