jupyter-notebook - 你好，当我运行我的代码时，我有这个错误模块'tensorflow'没有属性'get_default_graph'

Question

这是我的节点：

# Model Definition
input_shape = X_train[0].shape
num_genres = 10

def cnn_vgg16(input_shape, num_genres, freezed_layers):
    input_tensor = Input(shape=input_shape)
    vgg16 = VGG16(include_top=False, weights='imagenet',
                  input_tensor=input_tensor)

    top = Sequential()
    top.add(Flatten(input_shape=vgg16.output_shape[1:]))
    top.add(Dense(256, activation='relu'))
    top.add(Dropout(0.5))
    top.add(Dense(num_genres, activation='softmax'))

    model = Model(inputs=vgg16.input, outputs=top(vgg16.output))
    for layer in model.layers[:freezed_layers]:
        layer.trainable = False

    return model

model = cnn_vgg16(input_shape, num_genres, 5)
print("Creating EarlyStopping Callback ...")
early_stopping_callback = EarlyStopping(monitor='val_acc', patience=5)
model.summary()

这是错误：

AttributeError：模块“tensorflow”没有属性“get_default_graph”

标签： jupyter-notebookpython-3.7

Answer 1

我能够使用 Tensorflow 2.5 执行您的代码，如下所示

import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Flatten, Dense, Dropout
from tensorflow.keras import Sequential, Model, Input

input_shape = (224,224,3)
num_genres = 10

def cnn_vgg16(input_shape, num_genres, freezed_layers):
    input_tensor = Input(shape=input_shape)
    vgg16 = VGG16(include_top=False, weights='imagenet',
                  input_tensor=input_tensor)

    top = Sequential()
    top.add(Flatten(input_shape=vgg16.output_shape[1:]))
    top.add(Dense(256, activation='relu'))
    top.add(Dropout(0.5))
    top.add(Dense(num_genres, activation='softmax'))

    model = Model(inputs=vgg16.input, outputs=top(vgg16.output))
    for layer in model.layers[:freezed_layers]:
        layer.trainable = False

    return model

model = cnn_vgg16(input_shape, num_genres, 5)

model.summary()

输出：

2.5.0
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
sequential (Sequential)      (None, 10)                6425354   
=================================================================
Total params: 21,140,042
Trainable params: 21,027,466
Non-trainable params: 112,576
_________________________________________________________________