[英]Error when trying to fit model - Tensorflow CNN
I'm trying to create a CNN for image classification (Cats & Dogs).我正在尝试为图像分类(猫和狗)创建一个 CNN。 Everything was working fine untill the moment I used fit.在我使用 fit 之前,一切都运行良好。 Since I am a beginner I fear I haven't correctly created my sequential model.由于我是初学者,我担心我没有正确创建我的顺序模型。 I'm also not sure if my steps per epoch and validation steps are correct.我也不确定我每个时期的步骤和验证步骤是否正确。
I get the following error when I use history = model.fit:使用 history = model.fit 时出现以下错误:
2022-07-12 12:10:04.374122: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/15
2022-07-12 11:57:57.465865: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)>
2022-07-12 11:57:58.808025: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8201
2022-07-12 11:58:00.536214: W tensorflow/core/framework/op_kernel.cc:1680] Invalid argument: required broadcastable shapes
2022-07-12 11:58:00.536457: W tensorflow/core/framework/op_kernel.cc:1680] Invalid argument: required broadcastable shapes
2022-07-12 11:58:00.536536: W tensorflow/core/framework/op_kernel.cc:1680] Invalid argument: required broadcastable shapes
Traceback (most recent call last):
File "C:\Users\myPC\PycharmProjects\pythonProject\catsdogs.py", line 75, in <module>
history = model.fit(train_data_gen, epochs=epochs, validation_data=val_data_gen, steps_per_epoch=int(np.ceil(train_data_gen.n / float(batch_size))), validation_steps=int(np.ceil(val_data_gen.n / float(batch_size))))
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py", line 1184, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__
result = self._call(*args, **kwds)
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py", line 950, in _call
return self._stateless_fn(*args, **kwds)
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py", line 3039, in __call__
return graph_function._call_flat(
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py", line 1963, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call
outputs = execute.execute(
File "C:\Users\myPC\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: required broadcastable shapes
[[node Equal (defined at \PycharmProjects\pythonProject\catsdogs.py:75) ]] [Op:__inference_train_function_733]
Function call stack:
train_function
2022-07-12 11:58:00.649539: W tensorflow/core/kernels/data/generator_dataset_op.cc:107] 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>
This is the code I use(Tensorflow version is 2.6):这是我使用的代码(Tensorflow 版本是 2.6):
import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPool2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import numpy as np
import matplotlib.pyplot as plt
# GET PROJECT FILES
PATH = 'cats_and_dogs'
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
test_dir = os.path.join(PATH, 'test')
# GET NUMBER OF FILES IN EACH DIRECTORY
total_train = sum([len(files) for r, d, files in os.walk(train_dir)])
total_val = sum([len(files) for r, d, files in os.walk(validation_dir)])
total_test = len(os.listdir(test_dir))
# VARIABLES FOR PRE-PROCESSING AND TRAINING.
batch_size = 128
epochs = 15
IMG_HEIGHT = 150
IMG_WIDTH = 150
# CREATE IMAGE DATA GENERATORS
train_image_generator = ImageDataGenerator(rotation_range=0.5, zoom_range=0.2, horizontal_flip=True, vertical_flip=True,
rescale=1. / 255)
validation_image_generator = ImageDataGenerator(rescale=1. / 255)
test_image_generator = ImageDataGenerator(rescale=1. / 255)
train_data_gen = train_image_generator.flow_from_directory(directory=train_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary', batch_size=batch_size)
val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary', batch_size=batch_size)
test_data_gen = test_image_generator.flow_from_directory(directory=test_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary', batch_size=batch_size,
shuffle=False)
# CREATE MODEL
model = Sequential(
[
Conv2D(32, (3, 3), input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)),
MaxPool2D((2, 2)),
Dense(1, activation='relu')
]
)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=['accuracy'])
model.summary()
history = model.fit(train_data_gen, epochs=epochs, validation_data=val_data_gen, steps_per_epoch=int(np.ceil(train_data_gen.n / float(batch_size))), validation_steps=int(np.ceil(val_data_gen.n / float(batch_size))))
I feel like required broadcastable shapes
is the culprit but I might be wrong.我觉得required broadcastable shapes
是罪魁祸首,但我可能错了。
After a bit of research, I finally made it work in pyCharm as well.经过一番研究,我终于让它在 pyCharm 中也能正常工作。 The problem was I hadn't used Flatten.问题是我没有使用 Flatten。 Here is the piece of my code that I changed.这是我更改的代码。 It works fine now.它现在工作正常。 I'm still not sure why it wouldn't give me an error in Colab though.我仍然不确定为什么它不会在 Colab 中给我一个错误。
model = Sequential(
[
keras.Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3)),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, activation='relu'),
MaxPooling2D(),
Conv2D(128, 3, activation='relu'),
Flatten(),
Dense(64, activation='relu'),
Dropout(0.5),
Dense(1, activation='sigmoid')
]
)
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=3e-4),
loss=keras.losses.BinaryCrossentropy(),
metrics=["accuracy"]
)
model.summary()
history = model.fit(train_data_gen, epochs=epochs, validation_data=val_data_gen, batch_size=batch_size, verbose=2)
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