为什么Keras在这种情况下抱怨输入形状不兼容? [英] Why is Keras complaining about incompatible input shape in this case?

问题描述

我使用以下输入层训练了基于Keras的自动编码器模型:

I have trained a Keras-based autoencoder model with the following input layer:

depth = 1
width = height = 100
input_shape = (height, width, depth)
inputs = Input(shape=input_shape)
# rest of network definition ...

我的训练图像的宽度和高度是100像素灰度，因此深度为1.现在，我想在另一个脚本中加载训练后的模型，在其中加载图像，调整大小并将其发送到Keras模型:

Width and height of my training images were 100 pixels in grayscale, thus with a depth of 1. Now I want to load my trained model in another script, load an image there, resize and send it to the Keras model:

size = 100
image = cv2.imread(args.image, cv2.IMREAD_GRAYSCALE)
image = cv2.resize(image, (size, size), interpolation=cv2.INTER_AREA)
image = image.astype("float32") / 255.0
image = np.expand_dims(image, axis=-1)
# at this point image.shape = (100, 100, 1)
recon = autoencoder.predict(image)

但是，对 autoencoder.predict(image)的调用导致以下错误:

However, the call to autoencoder.predict(image) leads to the following error:

WARNING:tensorflow:Model was constructed with shape (None, 100, 100, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 100, 100, 1), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None, 100, 1, 1).

我不明白这一点，因为调用 predict()时图像的形状是(100，100，1)，对我来说很好.为什么Keras抱怨(None，100，1，1)的输入形状不兼容?

I don't understand this, as the shape of the image when calling predict() is (100, 100, 1), which looks fine to me. Why is Keras complaining about an incompatible input shape of (None, 100, 1, 1)?

推荐答案

这些简单的代码行会产生错误

These simple lines of code generate the error

X = np.random.uniform(0,1, (100,100,1))

inp = Input((100,100,1))
out = Dense(1)(Flatten()(inp))
model = Model(inp, out)

model.predict(X)

这是因为您的Keras模型期望数据采用以下格式(n_sample，100，100，1)

This is because your Keras model expects data in this format (n_sample, 100, 100, 1)

当您预测单个图像时，简单的重塑就能达到目的

A simple reshape when you predict a single image does the trick

model.predict(X.reshape(1,100,100,1))

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