训练使用 tensorflow.keras.Model 和 keras 函数式 API 设计的网络会导致 Python 崩溃

Question

-Keras Version: 2.3.1 -Tensorflow Version: 2.2.0 -OS: Windows 10 -Running on: CPU Processor Intel(R) Core(TM) i7-8850H -Developed in: PyCharm -Python Version: 3.7

I've made a few attempts at training a network designed using the keras functional API, however training always causes python to crash with the following exit message:

Process finished with exit code -1073740791 (0xC0000409)

Not other stack trace is provided. Below is a simplified version of the code I'm attempting to run:

import scipy.io as io
import os
import numpy as np
from tensorflow.keras import layers, losses, Input
from tensorflow.keras.models import Model

directory = 'C:\\Dataset'

def simple_generator(dim1, dim2, dim3, batch_size=5):
    while True:
        samples = np.random.random_sample((batch_size, dim1, dim2, dim3))
        targets = np.random.random_sample((batch_size, 3))
        yield samples, targets

example_shape = (1100, 4096, 2)

train_gen = simple_generator(example_shape[0], example_shape[1], example_shape[2], batch_size=10)
val_gen = simple_generator(example_shape[0], example_shape[1], example_shape[2])
test_gen = simple_generator(example_shape[0], example_shape[1], example_shape[2])

input_tensor = Input(shape=example_shape)
preprocess = layers.Conv2D(32, 3, activation='relu')(input_tensor)
preprocess = layers.Conv2D(32, 3, activation='relu')(preprocess)
preprocess = layers.MaxPool2D(pool_size=(3, 3), strides=3)(preprocess)

"""   Head 1   """
head_1 = layers.Conv2D(32, 3, activation='relu')(preprocess)
head_1 = layers.Conv2D(32, 3, activation='relu')(head_1)
head_1 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_1)
head_1 = layers.Conv2D(32, 3, activation='relu')(head_1)
head_1 = layers.Conv2D(32, 3, activation='relu')(head_1)
head_1 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_1)
head_1 = layers.Conv2D(16, 3, activation='relu')(head_1)
head_1 = layers.Conv2D(16, 3, activation='relu')(head_1)
head_1 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_1)
head_1 = layers.Dense(8, activation='relu')(head_1)

"""   Head 2   """
head_2 = layers.Conv2D(32, 3, activation='relu')(preprocess)
head_2 = layers.Conv2D(32, 3, activation='relu')(head_2)
head_2 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_2)
head_2 = layers.Conv2D(32, 3, activation='relu')(head_2)
head_2 = layers.Conv2D(32, 3, activation='relu')(head_2)
head_2 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_2)
head_2 = layers.Conv2D(16, 3, activation='relu')(head_2)
head_2 = layers.Conv2D(16, 3, activation='relu')(head_2)
head_2 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_2)
head_2 = layers.Dense(8, activation='relu')(head_2)

"""   Head 3   """
head_3 = layers.Conv2D(32, 3, activation='relu')(preprocess)
head_3 = layers.Conv2D(32, 3, activation='relu')(head_3)
head_3 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_3)
head_3 = layers.Conv2D(32, 3, activation='relu')(head_3)
head_3 = layers.Conv2D(32, 3, activation='relu')(head_3)
head_3 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_3)
head_3 = layers.Conv2D(16, 3, activation='relu')(head_3)
head_3 = layers.Conv2D(16, 3, activation='relu')(head_3)
head_3 = layers.MaxPool2D(pool_size=(3, 3), strides=3)(head_3)
head_3 = layers.Dense(8, activation='relu')(head_3)

concat_out = layers.Concatenate(axis=-1)([head_1, head_2, head_3])
concat_out = layers.Flatten()(concat_out)
output_tensor = layers.Dense(3)(concat_out)
model = Model(input_tensor, output_tensor)

model.compile(loss=losses.mean_absolute_error, optimizer='sgd')
model.summary()

history = model.fit(x=train_gen, steps_per_epoch=25, epochs=12, validation_data=val_gen, validation_steps=10, verbose=True)
model.save(directory + '\\divergent_network.h5')
evaluations = model.evaluate_generator(generator=test_gen, steps=20, verbose=True)
print(evaluations)

Can anyone explain the crash or suggest any potential solutions? I run this code in an Anaconda environment, however I have tried running in a basic python virtual environment to see if it was an Anaconda issue, but I saw no difference between the two.

Answer 1

It seems to have been an issue with my conda environment. I made a new "bare minimum" environment with only the packages I needed and that cleared everything up. The info I used to do this can be found at:

https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands

https://www.jetbrains.com/help/pycharm/creating-virtual-environment.html