在张量模型中输入多个输入和输出？

Question

我已经创建了多层模型，现在我想用数百个值来教它，这样它就可以预测来自不同输入的输出。但是我应该如何实现这些输入？我现在尝试在数组中创建一些数组。并使用训练功能一一输入和输出。但似乎在第二次重新教学时，它只正确地预测了第二个答案。也许我不明白这个概念？

import tensorflow as tf
import numpy as np

print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution: {}".format(tf.executing_eagerly()))

x = np.array([[[10, 10, 30, 20], [20, 10, 20, 10],]])

y = np.array([[[10, 10, 100, 10], [100, 10, 10, 10],]])


class Model(object):
    def __init__(self, x, y):
        # get random values.
        self.W = tf.Variable(tf.random.normal((len(x), len(x[0][0]))))
        self.b = tf.Variable(tf.random.normal((len(y),)))
        self.W1 = tf.Variable(tf.random.normal((len(x), len(x[0][0]))))
        self.b1 = tf.Variable(tf.random.normal((len(y),)))
        self.W2 = tf.Variable(tf.random.normal((len(x), len(x[0][0]))))
        self.b2 = tf.Variable(tf.random.normal((len(y),)))

    def __call__(self, x):
        out1 = tf.multiply(x, self.W) + self.b
        out2 = tf.multiply(out1, self.W1) + self.b1
        last_layer = tf.multiply(out2, self.W2) + self.b2
        # Input_Leyer = self.W * x + self.b
        return last_layer


def loss(predicted_y, desired_y):
    return tf.reduce_sum(tf.square(predicted_y - desired_y))


optimizer = tf.optimizers.Adam(0.1)


# noinspection PyPep8Naming
def train(model, inputs, outputs):
    with tf.GradientTape() as t:
        current_loss = loss(model(inputs), outputs)
    grads = t.gradient(current_loss, [model.W, model.b, model.W1, model.b1, model.W2, model.b2])
    optimizer.apply_gradients(zip(grads, [model.W, model.b, model.W1, model.b1, model.W2, model.b2]))

    print(current_loss)


model = Model(x, y)


for i in range(5000):
    train(model, x[0][0], y[0][0])
for i in range(10000):
    train(model, x[0][1], y[0][1])



for i in range(3):
    InputX = np.array([
        [input(), input(), input(), input()],
    ])
    #returning = tf.math.multiply(InputX, model.W, name=None )
    first = tf.multiply(InputX, model.W)
    second = tf.multiply(first, model.W1)
    returning = tf.multiply(second, model.W2)

    print("I predict:", returning)

Answer 1

您必须提供混合数据：

for i in range(5000):
    train(model, x[0][0], y[0][0])
    train(model, x[0][1], y[0][1])