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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>
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1 回答 1

0

一个步骤需要一整批图像，即如果您batch_size是 32 步，则在 250 步 (250 * 32 = 8000) 后数据用完。像这样设置你的steps_per_epoch和validation_steps：

model.fit_generator(
    training_set,
    steps_per_epoch=8000//32,
    epochs=25,
    validation_data=test_set,
    validation_steps=2000//32
 )
于 2020-03-27T00:46:41.213 回答