如何使张量流模型将列表作为输入？

编程问答 2022-08-14

问题描述

我是tensorflow的新手，我正在制作一个可以进行乘法运算的AI，
我需要使它能够使我的模型将列表作为输入。

这是我的代码：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

multiplication_q = np.array([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[11,[27,[30,[17,22],[20,13],[21,[19,24],19],11],15],12],[15,[18,[49,[12,4]],dtype=object)
multiplication_a = np.array([100,1,4,9,16,25,36,49,64,96,110,60,12,374,104,84,456,209,20,121,15,24,45,343,35,48],dtype=float)


model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=4,input_shape=[1]),tf.keras.layers.Dense(units=4),tf.keras.layers.Dense(units=1)
])

model.compile(loss='mean_squared_error',optimizer=tf.keras.optimizers.Adam(0.1))

history = model.fit(multiplication_q,multiplication_a,epochs=750,verbose=False)

print(model.predict([4,5]))

，这是错误消息：

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self,iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step,args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn,args,kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args,**kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x,training=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
        self.name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
        ' but received input with shape ' + str(shape))

    ValueError: Input 0 of layer sequential_10 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape [32,2]

解决方法

要解决您的问题，您应该做三件事：

1-像这样将dtype中的multiplication_q从object更改为int：

multiplication_q = np.array([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[11,[27,[30,[17,22],[20,13],[21,[19,24],19],11],15],12],[15,[18,[49,[12,4]],dtype=int)

2-在模型的第一个密集层中，使用input_shape=(2,)代替input_shape=[1]，如下所示：

model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=4,input_shape=(2,)),tf.keras.layers.Dense(units=4),tf.keras.layers.Dense(units=1)
])

3-对于预测函数，您应该传递list的{​​{1}}而不是list，因为您使用{{1}的list进行了训练} 

list
,

尝试将第一密集层中的输入设置为multiplication_q.shape，将输入形状设置为1时将输入形状设置为32,2

编辑：下面的代码解决了您的问题，尽管由于它不是很准确，所以您必须尝试一些东西。

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

multiplication_q = np.asarray([[10,4]])
multiplication_a = np.asarray([100,1,4,9,16,25,36,49,64,96,110,60,12,374,104,84,456,209,20,121,15,24,45,343,35,48])


multiplication_q = multiplication_q/np.amax(multiplication_q)
multiplication_a = multiplication_a/np.amax(multiplication_a)


model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(2,)))
model.add(tf.keras.layers.Dense(32,activation='relu'))
model.add(tf.keras.layers.Dense(units=1))


model.compile(loss='mean_squared_error',optimizer=tf.keras.optimizers.Adam(0.1))

history = model.fit(multiplication_q,multiplication_a,epochs=750)

print(model.predict(np.asarray([[4,5]])/np.amax(multiplication_q)*np.amax(multiplication_a)))
google-colaboratorypythonpython-3.xtensorflow