class lenet_5(tf.keras.Model):
    def  __init__(self,input_shape,outputs, **kwargs):
        super().__init__(**kwargs)
        self.conv1 = tf.keras.layers.Conv2D(filters=6,strides=(1,1),kernel_size=(5,5),activation="tanh")
        self.pool1 = tf.keras.layers.AveragePooling2D(pool_size=(2,2),strides=(2,2))
        self.conv2 = tf.keras.layers.Conv2D(filters=6,strides=(1,1),kernel_size=(5,5),activation="tanh")
        self.pool2 = tf.keras.layers.AveragePooling2D(pool_size=(2,2),strides=(2,2))
        self.flatten = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(units=84,activation="tanh")
        self.output_layer = tf.keras.layers.Dense(units=outputs,activation="softmax")

    def call(self,inputs):
        x = self.conv1(inputs)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.output_layer(x)
        return x

model = lenet_5((28,28,1),10)
model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.SGD(learning_rate=config["learning_rate"]), metrics=['accuracy'])

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