Keras简单使用在keras中建立模型测试自己的图片一些有用的函数(持续更新)

 def model(input_shape):

     # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!

     X_input = Input(input_shape)

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     # Zero-Padding: pads the border of X_input with zeroes

     X = ZeroPadding2D((3, 3))(X_input)

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     # CONV -> BN -> RELU Block applied to X

     X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)

     X = BatchNormalization(axis = 3, name = 'bn0')(X)

     X = Activation('relu')(X)

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     # MAXPOOL

     X = MaxPooling2D((2, 2), name='max_pool')(X)

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     # FLATTEN X (means convert it to a vector) + FULLYCONNECTED

     X = Flatten()(X)

     X = Dense(1, activation='sigmoid', name='fc')(X)

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     # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.

     model = Model(inputs = X_input, outputs = X, name='HappyModel')

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 return model

 step 1:

 happyModel = HappyModel(X_train.shape[1:]) # 只保留一个例子

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 step 2:

 happyModel.compile(optimizer = 'sgd', loss = 'binary_crossentropy', metrics = ['accuracy'])

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 step 3：

 happyModel.fit(x = X_train,y = Y_train, epochs = 5, batch_size = 16)

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 step 4：

 preds =  happyModel.evaluate(x = X_test, y = Y_test)

 # preds[0] = Loss

 # preds[1] = Test Accuracy

 1 img_path = 'your picture path'

 2 img = image.load_img(img_path, target_size=(64, 64))

 3 imshow(img)

 4 ​

 5 x = image.img_to_array(img)

 6 x = np.expand_dims(x, axis=0)

 7 x = preprocess_input(x)

 8 ​

 9 print(happyModel.predict(x))