我是 tensorflow 的新手，我正在制作一個可以進行乘法運算的 AI，我需要制作它以便我的模型可以將列表作為輸入。這是我的代碼：import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltmultiplication_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],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]], dtype=object)multiplication_a = np.array([100,1,4,0,9,16,25,36,49,64,96,0,110,0,60,12,374,0,104,84,456,209,16,20,121,15,24,45,0,343,35,48], 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]))這是錯誤消息：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)
2 回答
撒科打諢
TA貢獻1934條經驗 獲得超2個贊
要解決您的問題，您應該做三件事：
1- 將dtypefrom更改為這樣multiplication_q：objectint
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],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]], dtype=int)
2- 在模型的第一個 Dense 層中使用input_shape=(2,)而不是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- 對于預測函數，你應該傳遞一個listoflist而不是 a ，因為你用oflist進行了訓練listlist
model.predict([[4, 5]])
慕的地8271018
TA貢獻1796條經驗 獲得超4個贊
嘗試將第一個密集層中的輸入設置為，將輸入multiplication_q.shape形狀設置為132, 2
編輯：下面的代碼解決了您的問題，盡管您將不得不嘗試一些東西，因為它不是很準確。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
multiplication_q = np.asarray([[10,10],[1,1],[2,2],[0,0],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],[9,9],[1,0],[11,10],[27,0],[30,2],[4,3],[17,22],[20,0],[8,13],[21,4],[19,24],[11,19],[8,2],[4,5],[11,11],[1,15],[2,12],[15,3],[18,0],[49,7],[5,7],[12,4]])
multiplication_a = np.asarray([100,1,4,0,9,16,25,36,49,64,96,0,110,0,60,12,374,0,104,84,456,209,16,20,121,15,24,45,0,343,35,48])
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)))
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