Dont understand the error messaage and how to solve it-CodePudding

I have the following code to try to recompile the last layer of the mobilenetv2 to get a face recognition:


import tensorflow as tf

import os
import numpy as np
import matplotlib.pyplot as plt

base_dir = "C:\\Users\\Maik\\Desktop\\dataset"



IMAGE_SIZE = 224

BATCH_SIZE = 5

data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1. / 255,
    validation_split=0.2)


train_generator = data_generator.flow_from_directory(
    base_dir,
    target_size=(IMAGE_SIZE, IMAGE_SIZE),
    batch_size=BATCH_SIZE,
    subset='training')

# Create a validation generator
val_generator = data_generator.flow_from_directory(
    base_dir,
    target_size=(IMAGE_SIZE, IMAGE_SIZE),
    batch_size=BATCH_SIZE,
    subset='validation')

# Triggering a training generator for all the batches
for image_batch, label_batch in train_generator:
    break

# This will print all classification labels in the console
print(train_generator.class_indices)

# Creating a file which will contain all names in the format of next lines
labels = '\n'.join(sorted(train_generator.class_indices.keys()))

# Writing it out to the file which will be named 'labels.txt'
with open('labels.txt', 'w') as f:
    f.write(labels)



# Resolution of images (Width , Height, Array of size 3 to accommodate RGB Colors)
IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)


base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                               include_top=False,
                                               weights='imagenet')



model = tf.keras.Sequential([
    base_model,  # 1
    tf.keras.layers.Conv2D(32, 3, activation='relu'),  # 2
    tf.keras.layers.Dropout(0.2),  # 3
    tf.keras.layers.GlobalAveragePooling2D(),  # 4
    tf.keras.layers.Dense(3, activation='softmax')  # 5
])


model.summary()

# Printing some statistics
print('Number of trainable variables = {}'.format(len(model.trainable_variables)))

# Train the model

epochs = 10

# Fitting / Training the model
history = model.fit(train_generator,
                    epochs=epochs,
                    validation_data=val_generator)



base_model.trainable = True


print("Number of layers in the base model: ", len(base_model.layers))


fine_tune_at = 100


for layer in base_model.layers[:fine_tune_at]:
    layer.trainable = False


model.compile(loss='categorical_crossentropy',
              optimizer=tf.keras.optimizers.Adam(1e-5),
              metrics=['accuracy'])


model.summary()

print('Number of trainable variables = {}'.format(len(model.trainable_variables)))


history_fine = model.fit(train_generator,
                         epochs=5,
                         validation_data=val_generator
                         )

saved_model_dir = 'save/fine_tuning.h5'
model.save(saved_model_dir)
print("Model Saved to save/fine_tuning.h5")

'''
#tf.saved_model.save(model, saved_model_dir)
converter = tf.lite.TFLiteConverter.from_keras_model_file(model)
//Use this if 238 fails tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

and get the following error message:

Traceback (most recent call last):
  File "C:\Users\Maik\PycharmProjects\FR_Test\main.py", line 149, in <module>
    history = model.fit(train_generator,
  File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
    File "C:\Users\Maik\PycharmProjects\FR_Test\main.py", line 149, in <module>
      history = model.fit(train_generator,
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 1564, in fit
      tmp_logs = self.train_function(iterator)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 1160, in train_function
      return step_function(self, iterator)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 1146, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 1135, in run_step
      outputs = model.train_step(data)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 994, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
      return self.compiled_loss(
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\losses.py", line 152, in __call__
      losses = call_fn(y_true, y_pred)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\losses.py", line 272, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\losses.py", line 1990, in categorical_crossentropy
      return backend.categorical_crossentropy(
    File "C:\Users\Maik\PycharmProjects\FR_Test\venv\lib\site-packages\keras\backend.py", line 5535, in categorical_crossentropy
      return tf.nn.softmax_cross_entropy_with_logits(
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[5,4] labels_size=[5,6]
     [[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_8675]
2022-11-09 18:47:37.818932: W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.
     [[{{node PyFunc}}]]

Process finished with exit code 1

Has anyone encountered that before and can give me any pointers as to why this hapens?

CodePudding user response：

The main error in your code is that you are calling the fit method before and after compile method.

fit method should only be called after compile method.

So, remove these lines.

# Fitting / Training the model
history = model.fit(train_generator,
                    epochs=epochs,
                    validation_data=val_generator)

CodePudding user response：

the answer was to include this

from PIL import Image, ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True

as there apparently were different image encodings