tensorflow.keras.layers keras.Model 自定义层应用

忘是亡心i 2022-09-08 00:09 235阅读 0赞

# tensorflow.keras.layers keras.Model 自定义层应用 #

import  tensorflow as tf
    from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
    from    tensorflow import keras

数据预处理

def preprocess(x, y):
        """ x is a simple image, not a batch """
        x = tf.cast(x, dtype=tf.float32) / 255.
        x = tf.reshape(x, [28*28])
        y = tf.cast(y, dtype=tf.int32)
        y = tf.one_hot(y, depth=10)
        return x,y

自定义Layer

class MyDense(layers.Layer):
    
    	def __init__(self, inp_dim, outp_dim):
    		super(MyDense, self).__init__()
    
    		self.kernel = self.add_weight('w', [inp_dim, outp_dim])
    		self.bias = self.add_weight('b', [outp_dim])
    
    	def call(self, inputs, training=None):
    
    		out = inputs @ self.kernel + self.bias
    
    		return out

自定义Model

> 继承keras.Model可使用complie fit 等方法

class MyModel(keras.Model):
    
    	def __init__(self):
    		super(MyModel, self).__init__()
            # 自定义5层网络
    		self.fc1 = MyDense(28*28, 256)
    		self.fc2 = MyDense(256, 128)
    		self.fc3 = MyDense(128, 64)
    		self.fc4 = MyDense(64, 32)
    		self.fc5 = MyDense(32, 10)
    
    	def call(self, inputs, training=None):
            # 定义网络传播过程
    		x = self.fc1(inputs)
    		x = tf.nn.relu(x)
    		x = self.fc2(x)
    		x = tf.nn.relu(x)
    		x = self.fc3(x)
    		x = tf.nn.relu(x)
    		x = self.fc4(x)
    		x = tf.nn.relu(x)
    		x = self.fc5(x) 
    
    		return x

batchsz = 128
    (x, y), (x_val, y_val) = datasets.mnist.load_data()
    print('datasets:', x.shape, y.shape, x.min(), x.max())
    
    
    db = tf.data.Dataset.from_tensor_slices((x,y))
    db = db.map(preprocess).shuffle(60000).batch(batchsz)
    ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
    ds_val = ds_val.map(preprocess).batch(batchsz) 
    
    sample = next(iter(db))
    print(sample[0].shape, sample[1].shape)

datasets: (60000, 28, 28) (60000,) 0 255
    (128, 784) (128, 10)

创建模型

> summary()必须在fit 或 build之后使用

network = MyModel()
    network.build(input_shape=(None, 28*28))
    # summary()必须在fit 或 build之后使用
    network.summary()
    
    network.compile(optimizer=optimizers.Adam(lr=0.01),
    		loss=tf.losses.CategoricalCrossentropy(from_logits=True),
    		metrics=['accuracy']
    	)
    
    network.fit(db, epochs=5, validation_data=ds_val,
                  validation_freq=2)
    
    
    network.evaluate(ds_val)

Model: "my_model_8"_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================my_dense_40 (MyDense)        multiple                  200960    _________________________________________________________________my_dense_41 (MyDense)        multiple                  32896     _________________________________________________________________my_dense_42 (MyDense)        multiple                  8256      _________________________________________________________________my_dense_43 (MyDense)        multiple                  2080      _________________________________________________________________my_dense_44 (MyDense)        multiple                  330       =================================================================Total params: 244,522Trainable params: 244,522Non-trainable params: 0_________________________________________________________________Epoch 1/5469/469 [==============================] - 10s 22ms/step - loss: 0.3078 - accuracy: 0.9076Epoch 2/5469/469 [==============================] - 13s 28ms/step - loss: 0.1409 - accuracy: 0.9600 - val_loss: 0.1318 - val_accuracy: 0.9641Epoch 3/5469/469 [==============================] - 13s 27ms/step - loss: 0.1125 - accuracy: 0.9680Epoch 4/5469/469 [==============================] - 16s 35ms/step - loss: 0.0984 - accuracy: 0.9724 - val_loss: 0.1196 - val_accuracy: 0.9673Epoch 5/5469/469 [==============================] - 15s 32ms/step - loss: 0.0875 - accuracy: 0.976079/79 [==============================] - 3s 33ms/step - loss: 0.1224 - accuracy: 0.9704
    
    
    
    
    
    [0.12237951139141393, 0.9704]

模型预测

sample = next(iter(ds_val))x = sample[0]y = sample[1] # one-hotpred = network.predict(x) # [b, 10]# convert back to number y = tf.argmax(y, axis=1)pred = tf.argmax(pred, axis=1)print(pred)print(y)

tf.Tensor([7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 6 0 5 4 9 9 2 1 9 4 8 7 3 9 7 9 4 4 9 2 5 4 7 6 7 9 0 5], shape=(128,), dtype=int64)tf.Tensor([7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 6 0 5 4 9 9 2 1 9 4 8 7 3 9 7 4 4 4 9 2 5 4 7 6 7 9 0 5], shape=(128,), dtype=int64)