CODEWITHSUNDEEP
tensorflow2.0
fisakhan
fisakhan

Reputation: 802

You tried to call count_params on ..., but the layer isn't built. TensorFlow 2.0

I receive the following error in in Pyhotn 3 and TF 2.0.

"ValueError: You tried to call count_params on digits, but the layer isn't built. You can build it manually via: digits.build(batch_input_shape)." at line new_model.summary().

what is the problem and how to solve it?

inputs = keras.Input(shape=(784,), name='digits')
x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
x = layers.Dense(64, activation='relu', name='dense_2')(x)
outputs = layers.Dense(10, activation='softmax', name='predictions')(x)

model = keras.Model(inputs=inputs, outputs=outputs, name='3_layer_mlp')
model.summary()

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255

model.compile(loss='sparse_categorical_crossentropy',
              optimizer=keras.optimizers.RMSprop(),
              metrics=['accuracy'])
history = model.fit(x_train, y_train,
                    batch_size=64,
                    epochs=2)

model.save('saved_model', save_format='tf')
new_model = keras.models.load_model('saved_model')
new_model.summary()

Upvotes: 5

Views: 4859

Answers (4)

user2817725
user2817725

Reputation: 11

I met the same error, and it solved after upgraded tf2.0 to tf2.1.

Upvotes: 1

Anush
Anush

Reputation: 89

For 2.0 version Model can be saved in .h5 format, please use model.save('my_model.h5') while saving.

Please find the link of working gist.

Also issue seems to be resolved in the Latest TF-nightly version,as going forward 2.1 will be official version try using pip install tf-nightly

Find the link of working gist here.

Upvotes: 1

Baymax Young
Baymax Young

Reputation: 1

you can use the model properly if there is not the last line of you codes, that is to say you just can not use summary here.

Upvotes: 0

Viliami
Viliami

Reputation: 628

I had the same problem, I was using tensorflow==2.0.0. I tried running the same code using tensorflows nightly build (in my case pip install tf-nightly==2.1.0.dev20191003).

It worked on the nightly build but you may have to save the model again using the nightly build.

Upvotes: 0

Related Questions