Saving broken between tf.keras and Keras Core

[tirthasheshpatel]

Saving broken between tf.keras and Keras Core

[AYREB]

I'm not following. Would you mind elaborating on where I should insert the code for the get_weights()/set_weights() workaround. I am using a script to load and use the model and that's wear the issue is happening.

`import keras
import numpy as np
import tensorflow as tf
from keras.models import load_model
import matplotlib.pyplot as plt

class_names = ['apple', 'banana', 'beetroot', 'bell pepper', 'cabbage', 'capsicum', 'carrot', 'cauliflower', 'chilli pepper', 'corn', 'cucumber', 'eggplant', 'garlic', 'ginger', 'grapes', 'jalepeno', 'kiwi', 'lemon', 'lettuce', 'mango', 'onion', 'orange', 'paprika', 'pear', 'peas', 'pineapple', 'pomegranate', 'potato', 'raddish', 'soy beans', 'spinach', 'sweetcorn', 'sweetpotato', 'tomato', 'turnip', 'watermelon']

image_path = "download.jpg"

model = keras.models.load_model("TrainedModel.keras")

img = tf.keras.utils.load_img(
image_path, target_size=(180, 180)
)
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch

predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])

print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
`

[fchollet]

Also -- as a workaround, you can do weights = model.get_weights(); new_model.set_weights(weights). This will work.

[fchollet]

Can you print model.summary() on each side (before saving/loading)?

[tirthasheshpatel]

Here's the output of model summary.

Model summary before saving:

Model: "my_model"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Layer (type)                       ┃ Output Shape                  ┃     Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ input_layer (InputLayer)           │ (None, None, None, 3)         │           0 │
├────────────────────────────────────┼───────────────────────────────┼─────────────┤
│ conv2d (Conv2D)                    │ (None, None, None, 1)         │           4 │
└────────────────────────────────────┴───────────────────────────────┴─────────────┘
 Total params: 4 (16.00 B)
 Trainable params: 4 (16.00 B)
 Non-trainable params: 0 (0.00 B)

Model summary before loading:

Model: "my_model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, None, None, 3)]   0         
                                                                 
 conv2d (Conv2D)             (None, None, None, 1)     4         
                                                                 
=================================================================
Total params: 4 (16.00 Byte)
Trainable params: 4 (16.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

[fchollet]

This is probably caused by a naming discrepancy. We should fix it. @nkovela1 do you have cycles to take a look?

[fchollet]

I found the cause -- basically some change in TF seems to have broken the save file namespace in tf.keras (introduction of a _layer_checkpoint_dependencies path which overrides layers. We have to fix this on the tf.keras side. Things are nominal in Keras Core.

[nkovela1]

Sure thing, I just created a Colab to inspect the h5 file and found that same discrepancy with _layer_checkpoint_dependencies: https://colab.sandbox.google.com/drive/1Ir1AQp6DUtYXk-nomRVgjM11ukTPXnnt

[fchollet]

This is now fixed at HEAD but the issue will persist in TF 2.13 and TF 2.14. Use get_weights()/set_weights() as a workaround.

[tirthasheshpatel]

Sounds good, thanks for the quick fix!