CODEWITHSUNDEEP
pythontensorflowtransfer-learning
Lombiz
Lombiz

Reputation: 1

TensorFlow Hub: InvalidArgumentError

I visited: https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5

I went down to "Usage" and copied into colab:

m = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5",
                   trainable=False),  # Can be True, see below.
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

However I ran and still got this:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-66-52a976264686> in <module>()
      1 m = tf.keras.Sequential([
      2     hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5",
----> 3                    trainable=False),  # Can be True, see below.
      4     tf.keras.layers.Dense(num_classes, activation='softmax')
      5 ])

19 frames
/usr/local/lib/python3.7/dist-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /tmp/tfhub_modules/02229962626ef521d65cf8ce349d83f59c4e3f51/variables/variables: Unimplemented: File system scheme '[local]' not implemented (file: '/tmp/tfhub_modules/02229962626ef521d65cf8ce349d83f59c4e3f51/variables/variables') [Op:Identity]

What could I have done possibly wrong? I copied it exactly, TensorFlow was also imported as tf. Many thanks for any help.

Upvotes: 0

Views: 92

Answers (1)

user11530462
user11530462

Reputation:

You can execute code as shown below

import tensorflow as tf
print(tf.__version__)
import tensorflow_hub as hub
print(hub.__version__)

num_classes = 10

m = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5",
                   trainable=False),  # Can be True, see below.
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

m.build([None, 224, 224, 3])  # Batch input shape.
m.summary()

Output:

2.6.0
0.12.0
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer_1 (KerasLayer)   (None, 2048)              23564800  
_________________________________________________________________
dense (Dense)                (None, 10)                20490     
=================================================================
Total params: 23,585,290
Trainable params: 20,490
Non-trainable params: 23,564,800
_________________________________________________________________

Upvotes: 1

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