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[英]Error when checking input: expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (3, 224, 224)
[英]ValueError: Error when checking : expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (58000, 28, 28)
我已经成功构建了一个二进制分类器,并且正在使用它来预测一些数据。 所有数据都是大小为(28,28)的MNIST灰度数字,我有58000张图像。 我的代码如下:
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',color_mode = 'grayscale')
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',color_mode = 'grayscale')
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)
filelist = glob.glob('/Binary Classifier/data/image_data/*.png')
x = np.array([np.array(Image.open(fname)) for fname in filelist])
ones=model.predict(x)
我在ones = model.predict(x)处出错:
ValueError:检查错误:预期conv2d_1_input具有4个维,但数组的形状为(58000,28,28)
为了解决这个问题,我添加:
x = np.expand_dims(x, axis=0)
在执行最后一行之前。 但是现在我收到另一个错误:
ValueError:检查时出错:预期conv2d_1_input具有形状(28,28,1),但数组的形状为(58000,28,28)
我有两个困惑:
提前致谢!
在堆叠图像之前,将图像从(28,28)调整为(28,28,1)。 这样便得到了最终的形状数组(5800、28、28、1)。
或者也许你应该做
x = np.expand_dims(x, axis=3)
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