tensorflow(二十八)：Keras自定义层，继承layer,model

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

os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘2‘

def preprocess(x, y):
    """
    x is a simple image, not a batch
    :param x:
    :param y:
    :return:
    """
    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))
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)

iteration = iter(db)
sample = next(iteration)
print("迭代器获得为：", sample[0].shape, sample[1].shape)


class MyDense(layers.Layer):
    def __init__(self, inp_dim, outp_dim):
        super(MyDense, self).__init__()

        self.kernel = self.add_variable(‘w‘, [inp_dim, outp_dim])
        self.bias = self.add_variable(‘b‘, [outp_dim])

    def call(self, input, training=None):
        out = input @ self.kernel + self.bias
        return out

class MyModel(keras.Model):

    def __init__(self):
        super(MyModel, self).__init__()

        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):

        x = self.fc1(inputs)  ##fc1为一个instance,默认调用__call__()==> call()
        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

network = MyModel()

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)

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

print(pred)
print(y)