python - model.summary()-AttributeError : 'Tensor' object has no attribute 'summary'

Question

这是我的进口:

import tensorflow as tf
import keras
from keras.models import Sequential, Model
from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, GlobalAveragePooling2D, BatchNormalization, Dropout, Activation, GlobalMaxPooling2D
from keras.utils import Sequence

我定义了这个模型:

def create_ST_layer(input_shape = (64, 128, 3)):
    input_img = Input(shape=input_shape)
    model = Conv2D(48, kernel_size=(5, 5), input_shape = input_shape, strides = (1, 1), activation = "relu")(input_img)
    model = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(model)
    model = Conv2D(32, kernel_size=(5, 5), strides = (1, 1), activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(model)
    model = Dense(50, activation = "relu")(model)
    model = Dense(6)(model)

    return model

并通过以下方式创建模型:

model = create_ST_layer()

现在，当我尝试获取模型摘要时:

model.summary()

我收到以下错误:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-5f15418b3570> in <module>()
----> 1 model.summary()

AttributeError: 'Tensor' object has no attribute 'summary'

我的进口货有问题吗？
非常感谢!

Answer 1

我在Google Colab的tensorflow 2.2.0上对此进行了测试。
我会从几件事做起。使用新的tensorflow版本，而不是导入keras，您应该导入tensorflow.keras。
因此，对于导入，您的代码如下所示:

from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, GlobalAveragePooling2D, BatchNormalization, Dropout, Activation, GlobalMaxPooling2D
from tensorflow.keras.utils import Sequence

另外，您需要调用以下行，以将层分组为具有训练和推理功能的对象。 [模型链接]: https://www.tensorflow.org/api_docs/python/tf/keras/Model
因此，您的完整代码应如下所示:

import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, GlobalAveragePooling2D, BatchNormalization, Dropout, Activation, GlobalMaxPooling2D
from tensorflow.keras.utils import Sequence

def create_ST_layer(input_shape = (64, 128, 3)):
    input_img = Input(shape=input_shape)
    model = Conv2D(48, kernel_size=(5, 5), input_shape = input_shape, strides = (1, 1), activation = "relu")(input_img)
    model = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(model)
    model = Conv2D(32, kernel_size=(5, 5), strides = (1, 1), activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(model)
    model = Dense(50, activation = "relu")(model)
    model = Dense(6)(model)
    model = tf.keras.Model(inputs=input_img, outputs= model)
    return model

model = create_ST_layer()
model.summary()

您的模型得到以下输出:
enter image description here