Question

我正在阅读关于Keras的文档以及之前在StackOverFlow上的问题和回复。目前，这是我到目前为止：

#Creating base pre-trained model (Default Input Size for ResNet50 is (299, 299))
base_model = InceptionV3(weights = 'imagenet', include_top = False, input_shape = (299, 299, 3))


#Adding a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)

#Adding a fully-connected dense layer
x = Dense(1024, activation = 'relu')(x)

#Adding a logistic layer - We have 2 classes: Cats and Dogs
predictions = Dense(2, activation = 'softmax')(x)

#Model to be trained
model = Model(inputs = base_model.input, outputs = predictions)

#First trains only the top layers
#Freeze all convolutional ResNet50 layers
for layer in base_model.layers:
    layer.trainable = False

#Compile the model
model.compile(optimizer = 'rmsprop', loss = 'sparse_categorical_crossentropy')

#Train the model on the new data
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory("./data/train",
                                                    target_size = (299, 299),
                                                    batch_size = 25,
                                                    class_mode = 'binary')
model.fit_generator(train_generator, steps_per_epoch = 10)

#Train top 2 inception blocks by freezing first 249 layers and unfreezing the rest
for layer in model.layers[:249]:
    layer.trainable = False
for layer in model.layers[249:]:
    layer.trainable = True

#Now to recompile the model for modifications to take effect, use SGD
model.compile(optimizer = SGD(lr = 0.0001, momentum = 0.9), loss = 'sparse_categorical_crossentropy')

#Train model again, fine-tuning top 2 inception blocks alongside top Dense layers
model.fit_generator(train_generator, steps_per_epoch = 10)

#Test model
imges = glob.glob('./test/*.jpg')
for fname in imges:
    img_path = fname
    img = image.load_img(img_path, target_size=(299, 299))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    pred = model.predict(x)

    print('Predicted:', decode_predictions(pred, top=3)[0])

我目前收到一条错误，上面写着＃34;在decode_predictions中排第82行，价值错误：＆＃39; decode_predictions＆＃39;期望一批预测（即2D形状阵列（样本，1000））。找到形状为：（1,2）＆＃34;

的数组

有什么我忘了做的吗？我目前在我的智慧结束。任何和所有的帮助将不胜感激。提前谢谢

如何在Keras进行微调和测试InceptionV3？

0 个答案: