ValueError:层sequencial_1的输入0与层不兼容

Question

我在Keras中编写了以下模型，但在进行预测时，遇到了ValueError。我查看了StackOverflow上的其他问题，但无法准确地理解我的代码。

我的培训模式是：

#building the CNN model
cnn = Sequential()
kernelSize = (3, 3)
ip_activation = 'relu'
ip_conv_0 = Conv2D(filters=32, kernel_size=kernelSize, input_shape=im_shape, activation=ip_activation)
cnn.add(ip_conv_0)
# Add the next Convolutional+Activation layer
ip_conv_0_1 = Conv2D(filters=64, kernel_size=kernelSize, activation='relu')
cnn.add(ip_conv_0_1)
# Add the Pooling layer
pool_0 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")
cnn.add(pool_0)
ip_conv_1 = Conv2D(filters=64, kernel_size=kernelSize, activation='relu')
cnn.add(ip_conv_1)
ip_conv_1_1 = Conv2D(filters=64, kernel_size=kernelSize, activation='relu')
cnn.add(ip_conv_1_1)
pool_1 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")
cnn.add(pool_1)
# Let's deactivate around 20% of neurons randomly for training
drop_layer_0 = Dropout(0.2)
cnn.add(drop_layer_0)

flat_layer_0 = Flatten()
cnn.add(Flatten())
# Now add the Dense layers
h_dense_0 = Dense(units=128, activation='relu', kernel_initializer='uniform')
cnn.add(h_dense_0)
# Let's add one more before proceeding to the output layer
h_dense_1 = Dense(units=64, activation='relu', kernel_initializer='uniform')
cnn.add(h_dense_1)
op_activation = 'softmax'
output_layer = Dense(units=n_classes, activation='softmax', kernel_initializer='uniform')
cnn.add(output_layer)
opt = 'adam'
loss = 'categorical_crossentropy'
metrics = ['accuracy']
# Compile the classifier using the configuration we want
cnn.compile(optimizer=opt, loss=loss, metrics=metrics)
cnn_summary = cnn.summary()
history = cnn.fit(x_train, y_train,
batch_size=40, epochs=20,
validation_data=(x_test, y_test)
)

我尝试在另一个.py文件中使用以下代码进行预测：

import numpy as np
from keras.preprocessing import image 
from keras.models import load_model
model=load_model('trained_model.h5')
test_image = image.load_img('131.png', target_size=(32,32))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
pre = model.predict(test_image)

但问题是，我得到的值错误为：

ValueError: Input 0 of layer sequential_1 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape [None, 32, 32, 3]

有人能帮我解决这个错误吗？

Answer 1

它基本上说你的第一层期望(32, 32, 1)的形状

ip_conv_0 = Conv2D(filters=32, kernel_size=kernelSize, input_shape=im_shape, activation=ip_activation)

，所以这里是im_shape=(32,32,1)，但在预测时，它接收到形状为(32,32,3)的3通道图像。

我认为你用灰度图像训练了你的网络，并试图用彩色图像(RGB(进行推断，这不适合你构建的网络模型。你可以做的是，你可以用(32,32,3)形状的图像来训练你的模型，我认为这不是一个选项，或者你可以让你的RGB(彩色(图像变成灰度，这样你的图像就有了(32,32,1)形状，然后你可以用你的模型来推断。