How to get intermediate output for eager mode

I established a transfer learning model and tried to get the intermediate output. I followed one of the suggestion here in order to get the intermediate output of a model, as follows:

import tensorflow as tf

from tensorflow.keras import backend as K



tf.enable_eager_execution()



base_conv = tf.keras.applications.MobileNetV2(input_shape= None, 

            alpha=1.0, depth_multiplier=1, 

            include_top=True, weights='imagenet', input_tensor=None, 

            pooling=None, classes=1000)



model = tf.keras.models.Sequential()



model.add(base_conv)



model.add(tf.keras.layers.Dense(512, activation='relu'))



model.add(tf.keras.layers.Dense(5, activation='softmax'))



# with a Sequential model

get_layer_output = K.function([model.layers[0].input],                                 

                          [model.layers[0].get_layer('Conv_1').output])



#x is the input image

layer_output = get_layer_output([x])

But then I got an error:

AttributeError: Tensor.name is meaningless when eager execution is enabled.

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

add a comment |

I established a transfer learning model and tried to get the intermediate output. I followed one of the suggestion here in order to get the intermediate output of a model, as follows:

import tensorflow as tf

from tensorflow.keras import backend as K



tf.enable_eager_execution()



base_conv = tf.keras.applications.MobileNetV2(input_shape= None, 

            alpha=1.0, depth_multiplier=1, 

            include_top=True, weights='imagenet', input_tensor=None, 

            pooling=None, classes=1000)



model = tf.keras.models.Sequential()



model.add(base_conv)



model.add(tf.keras.layers.Dense(512, activation='relu'))



model.add(tf.keras.layers.Dense(5, activation='softmax'))



# with a Sequential model

get_layer_output = K.function([model.layers[0].input],                                 

                          [model.layers[0].get_layer('Conv_1').output])



#x is the input image

layer_output = get_layer_output([x])

But then I got an error:

AttributeError: Tensor.name is meaningless when eager execution is enabled.

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

add a comment |

I established a transfer learning model and tried to get the intermediate output. I followed one of the suggestion here in order to get the intermediate output of a model, as follows:

import tensorflow as tf

from tensorflow.keras import backend as K



tf.enable_eager_execution()



base_conv = tf.keras.applications.MobileNetV2(input_shape= None, 

            alpha=1.0, depth_multiplier=1, 

            include_top=True, weights='imagenet', input_tensor=None, 

            pooling=None, classes=1000)



model = tf.keras.models.Sequential()



model.add(base_conv)



model.add(tf.keras.layers.Dense(512, activation='relu'))



model.add(tf.keras.layers.Dense(5, activation='softmax'))



# with a Sequential model

get_layer_output = K.function([model.layers[0].input],                                 

                          [model.layers[0].get_layer('Conv_1').output])



#x is the input image

layer_output = get_layer_output([x])

But then I got an error:

AttributeError: Tensor.name is meaningless when eager execution is enabled.

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

I established a transfer learning model and tried to get the intermediate output. I followed one of the suggestion here in order to get the intermediate output of a model, as follows:

import tensorflow as tf

from tensorflow.keras import backend as K



tf.enable_eager_execution()



base_conv = tf.keras.applications.MobileNetV2(input_shape= None, 

            alpha=1.0, depth_multiplier=1, 

            include_top=True, weights='imagenet', input_tensor=None, 

            pooling=None, classes=1000)



model = tf.keras.models.Sequential()



model.add(base_conv)



model.add(tf.keras.layers.Dense(512, activation='relu'))



model.add(tf.keras.layers.Dense(5, activation='softmax'))



# with a Sequential model

get_layer_output = K.function([model.layers[0].input],                                 

                          [model.layers[0].get_layer('Conv_1').output])



#x is the input image

layer_output = get_layer_output([x])

But then I got an error:

AttributeError: Tensor.name is meaningless when eager execution is enabled.

python tensorflow eager

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

edited Nov 20 at 10:07

Milo Lu

1,57111327

edited Nov 20 at 10:07

Milo Lu

1,57111327

edited Nov 20 at 10:07

Milo Lu

1,57111327

asked Nov 20 at 4:12

Yixiang Cai

154

asked Nov 20 at 4:12

Yixiang Cai

154

asked Nov 20 at 4:12

Yixiang Cai

154

add a comment |

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