tensorflow.keras.Model compile fit evaluate应用

向右看齐 2022-09-08 00:10 193阅读 0赞

# tensorflow.keras.Model compile fit evaluate应用 #

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

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

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))
    # 使用preprocess函数处理数据集
    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)

模型构建

network = Sequential([layers.Dense(256, activation='relu'),
                         layers.Dense(128, activation='relu'),
                         layers.Dense(64, activation='relu'),
                         layers.Dense(32, activation='relu'),
                         layers.Dense(10)])
    network.build(input_shape=(None, 28*28))
    network.summary()

Model: "sequential_2"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense_10 (Dense)             multiple                  200960    
    _________________________________________________________________
    dense_11 (Dense)             multiple                  32896     
    _________________________________________________________________
    dense_12 (Dense)             multiple                  8256      
    _________________________________________________________________
    dense_13 (Dense)             multiple                  2080      
    _________________________________________________________________
    dense_14 (Dense)             multiple                  330       
    =================================================================
    Total params: 244,522
    Trainable params: 244,522
    Non-trainable params: 0
    _________________________________________________________________

使用compile设置模型的优化器，损失函数，metrics

network.compile(optimizer=optimizers.Adam(lr=0.01),
    		loss=tf.losses.CategoricalCrossentropy(from_logits=True),
    		metrics=['accuracy'] # accuracy用来计算准确度
    	)

使用fit配置模型的训练数据集，训练轮次，验证数据集，验证频次

network.fit(db, epochs=5, validation_data=ds_val, validation_freq=2)

Epoch 1/5469/469 [==============================] - 11s 24ms/step - loss: 0.2834 - accuracy: 0.9153Epoch 2/5469/469 [==============================] - 12s 27ms/step - loss: 0.1356 - accuracy: 0.9628 - val_loss: 0.1399 - val_accuracy: 0.9614Epoch 3/5469/469 [==============================] - 9s 19ms/step - loss: 0.1134 - accuracy: 0.9696Epoch 4/5469/469 [==============================] - 12s 26ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.1208 - val_accuracy: 0.9685Epoch 5/5469/469 [==============================] - 10s 21ms/step - loss: 0.0846 - accuracy: 0.9773
    
    
    
    
    
    <tensorflow.python.keras.callbacks.History at 0x7f893e44de90>

使用evaluate评估模型

network.evaluate(ds_val)

79/79 [==============================] - 3s 36ms/step - loss: 0.1085 - accuracy: 0.9738
    
    
    
    
    
    [0.10845260255486716, 0.9738]

使用predict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 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 0 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 9 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)