Keras Convolution2D输入：检查模型输入时出错：预期卷积2d_input_1具有形状

Question

我正在完成这个使用Keras创建图像分类器的精彩教程 。 一旦我训练了模型，我将其保存到一个文件中，然后将其重新加载到下面显示的测试脚本中的模型中。

当我使用新的，前所未见的图像评估模型时，我得到以下异常：

错误：

Traceback (most recent call last):
  File "test_classifier.py", line 48, in <module>
    score = model.evaluate(x, y, batch_size=16)
  File "/Library/Python/2.7/site-packages/keras/models.py", line 655, in evaluate
    sample_weight=sample_weight)
  File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 1131, in evaluate
    batch_size=batch_size)
  File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 959, in _standardize_user_data
exception_prefix='model input')
  File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 108, in standardize_input_data
str(array.shape))
Exception: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 150, 150) but got array with shape (1, 3, 150, 198)`

是我训练的模型的问题还是我如何调用evaluate方法？

码：

    from keras.preprocessing.image import ImageDataGenerator
    from keras.models import Sequential
    from keras.layers import Convolution2D, MaxPooling2D
    from keras.layers import Activation, Dropout, Flatten, Dense
    from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

    import numpy as np
    img_width, img_height = 150, 150
    train_data_dir = 'data/train'
    validation_data_dir = 'data/validation'
    nb_train_samples = 2000
    nb_validation_samples = 800
    nb_epoch = 5
    model = Sequential()
    model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(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'])
    model.load_weights('first_try.h5')
    img = load_img('data/test2/ferrari.jpeg')
    x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
    x = x.reshape( (1,) + x.shape )  # this is a Numpy array with shape (1, 3, 150, 150)
    y = np.array([0])
    score = model.evaluate(x, y, batch_size=16)`

Answer 1

问题是由于测试图像的大小错误。 为了我，

train_datagen.flow_from_directory(
        'C:\\Users\\...\\train',  # this is the target directory
        target_size=(150, 150),  # all images will be resized to 150x150
        batch_size=32,
        class_mode='binary')

工作不正常。 所以我使用matlab命令调整所有测试图像的大小，它工作正常

Answer 2

这个问题有两个方面：

1. 测试图像尺寸错误。 它是150 x 198，需要150 x 150。

2. 我不得不将密集层从model.add(Dense(10))更改为model.add(Dense(1)) 。

我还不知道如何让模型给我预测，但至少现在，模型评估运行。

Answer 3

我有同样的问题并使用此功能：目标文件夹（.jpg和.png）中的所有图像将调整为高度和宽度。 并除以255.另外增加了1个维度（所需的输入形状）。

from scipy import misc
import os

def readImagesAsNumpyArrays(targetPath, i_height, i_width):
    files = os.listdir(targetPath)
    npList = list()
    for file in files:
        if ".jpg" or ".png" in str(file):
            path = os.path.join(targetPath, file)
            img = misc.imread(path)
            img = misc.imresize(img, (i_height, i_width))
            img = img * (1. / 255)
            img = img[None, :, :,: ]
            npList.append(img)
    return npList