- #1

Gaussian97

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- TL;DR Summary
- I'm using the TensorFlow library in Python. After creating a model and saving it, if I load the entire model, I get inconsistent results.

First of all, I'm using TensorFlow version 2.3.0

The code I'm using is the following:

Until here no problem, I create the model, compile it and train it with some data. I also use ModelCheckpoint to save the model.

The problem comes when I try the following

Then, the first evaluation returns an accuracy of 0.477, while the other returns an accuracy of 0.128, which is essentially a random choice.

Where's the error? The two models are supposed to be identical, and actually, they give the same value for the loss function up to 16 decimal places.

The code I'm using is the following:

Python:

```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import ModelCheckpoint
def get_new_model():
model = Sequential([
Conv2D(filters=16, input_shape=(32, 32, 3), kernel_size=(3, 3), activation='relu', name='conv_1'),
Conv2D(filters=8, kernel_size=(3, 3), activation='relu', name='conv_2'),
MaxPooling2D(pool_size=(4, 4), name='pool_1'),
Flatten(name='flatten'),
Dense(units=32, activation='relu', name='dense_1'),
Dense(units=10, activation='softmax', name='dense_2')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
checkpoint_path = 'model_checkpoints'
checkpoint = ModelCheckpoint(filepath=checkpoint_path, save_weights_only=False, frequency='epoch', verbose=1)
model = get_new_model()
model.fit(x_train, y_train, epochs=3, callbacks=[checkpoint])
```

The problem comes when I try the following

Python:

```
from tensorflow.keras.models import load_model
model2 = load_model(checkpoint_path)
model.evaluate(x_test, y_test)
model2.evaluate(x_test, y_test)
```

Where's the error? The two models are supposed to be identical, and actually, they give the same value for the loss function up to 16 decimal places.