# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
# Initialising the CNN
chars74k_classifier = Sequential()
# Adding the first convolutional layer
chars74k_classifier.add(Conv2D(32, (3, 3), activation = 'relu', input_shape = (64, 64, 3)))
# Adding the max pooling layer
chars74k_classifier.add(MaxPooling2D(pool_size = (2, 2)))
chars74k_classifier.add(Dropout(0.25))
# Adding the second convolutional layer
chars74k_classifier.add(Conv2D(32, (3, 3), activation='relu'))
# Adding a second max pooling layer
chars74k_classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding the third convolutional layer
chars74k_classifier.add(Conv2D(64, (3, 3), activation='relu'))
# Adding a third max pooling layer
chars74k_classifier.add(MaxPooling2D(pool_size = (2, 2)))
chars74k_classifier.add(Dropout(0.50))
# Adding the fourth convolutional layer
chars74k_classifier.add(Conv2D(128, (3, 3), activation='relu'))
# Adding a fourth max pooling layer
chars74k_classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding the flattening layer
chars74k_classifier.add(Flatten())
# Adding the fully connected layers (Normal ANN)
chars74k_classifier.add(Dense(activation = 'relu', units = 128))
chars74k_classifier.add(Dense(activation = 'relu', units = 128))
chars74k_classifier.add(Dense(activation = 'softmax', units = 26))
# Compiling the CNN
chars74k_classifier.compile(optimizer='Adadelta',
loss='categorical_crossentropy',
metrics=['accuracy'])
这是我为Keras Convolutional神经网络编写的代码，在使用keras 2.0.6和tensorflow 1.1.0进行训练时，keras在测试集上获得了86％的高精度 . 当我将此模型导出到CoreML模型时，输入不是图像，而是多阵列？我如何解决这个问题，因为网络的输入实际上是带有颜色的64x64图像？
1 回答
在coremltools转换脚本中，指定
input_image_names="input"参数 .