Question

使用Tensorflow，我构建了一个二元分类模型：

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import tensorflow
import glob
from PIL import Image
import numpy as np

img_width, img_height = 28, 28#all MNIST images are of size (28*28)

train_data_dir = '/Binary Classifier/data/train'#train directory generated by train_cla
validation_data_dir = '/Binary Classifier/data/val'#validation directory generated by val_cla
train_samples = 40000
validation_samples = 10000
epochs = 2
batch_size = 512

if K.image_data_format() == 'channels_first':
    input_shape = (1, img_width, img_height)
else:
    input_shape = (img_width, img_height, 1)

#build a sequential model to train data
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(#train data generator
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

val_datagen = ImageDataGenerator(rescale=1. / 255)#validation data generator

train_generator = train_datagen.flow_from_directory(#train generator
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = val_datagen.flow_from_directory(#validation generator
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(#fit the generator to train and validate the model
    train_generator,
    steps_per_epoch=train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=validation_samples // batch_size)

但是我得到一个错误，说“ValueError：检查输入时出错：预期conv2d_1_input有形状（28,28,1）但是有形状的数组（28,28,3）”，我不明白哪里这个错误来自。我特意将输入形状定义为（28,28,1）或（28,28,1），并且我的所有输入数据都是MNIST数字，其大小也应为（28,28,1）。发生器如何接收（28,28,3）阵列？任何帮助表示赞赏！

Answer 1

ImageDataGenerator＆＃39; s flow_from_directory中的默认设置是以RGB格式加载彩色图像，这意味着三个通道。您希望将图像加载为灰度（一个通道），并且可以通过将color_mode中的flow_from_directory参数设置为grayscale来完成此操作。

train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary', color_mode = 'grayscale')

validation_generator = val_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary', color_mode = 'grayscale')

ValueError：检查输入时出错：预期conv2d_1_input具有形状（28,28,1）但是具有形状的数组（28,28,3）

1 个答案: