美好的一天，尝试学习CNN并在运行以下代码时遇到问题。
from tensorflow.keras.layers import Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import MaxPooling2D
import pandas as pd
import numpy as np
import matplotlib.pyplot
%matplotlib inline
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(64, 64, 3), activation='relu')
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))
model.compile(optimizer = 'rmsprop', loss='mse', metrics=['accuracy'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale = 1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory(
r'C:\Users\Raj Mulati\Downloads\Dev\Machine Learning A-Z New\Part 8 - Deep Learning\Section 40 -
Convolutional Neural Networks (CNN)\dataset\training_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
r'C:\\Users\Raj Mulati\\Downloads\\Dev\\Machine Learning A-Z New\Part 8 - Deep
Learning\\Section 40 - Convolutional Neural Networks (CNN)\\dataset\\test_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
model.fit_generator(
training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000
)
我得到的错误是：
Found 8000 images belonging to 2 classes.
Found 2000 images belonging to 2 classes.
WARNING:tensorflow:sample_weight modes were coerced from
...
to
['...']
WARNING:tensorflow:sample_weight modes were coerced from
...
to
['...']
Train for 8000 steps, validate for 2000 steps
Epoch 1/25
250/8000 [..............................] - ETA: 14:37 - loss: 0.2485 - accuracy: 0.5340WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 200000 batches). You may need to use the repeat() function when building your dataset.
<tensorflow.python.keras.callbacks.History at 0x234d9fec3c8>
一步需要完整的一批图像，即如果您batch_size是32位，则250个步骤后（250 * 32 = 8000），您的数据用完了。设置你steps_per_epoch和validation_steps这样的：
model.fit_generator(
training_set,
steps_per_epoch=8000//32,
epochs=25,
validation_data=test_set,
validation_steps=2000//32
)
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