【问题标题】:why tensorflow reshape array is out of range为什么张量流重塑数组超出范围
【发布时间】:2020-01-01 00:41:07
【问题描述】:

我有数组重塑和大小问题

我没有尝试任何事情,因为我还是新手,我不想搞砸与问题无关的事情

import tensorflow as tf
import numpy as np


mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)  # scales data between 0 and 1
x_test = tf.keras.utils.normalize(x_test, axis=1) 

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(32,)))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy', 
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=3)

val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss)
print(val_acc)
  File "t1.py", line 17, in <module>
    x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
  File "<__array_function__ internals>", line 6, in reshape
  File "H:\Program Files\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 301, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "H:\Program Files\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 61, in _wrapfunc
    return bound(*args, **kwds)
ValueError: cannot reshape array of size 47040000 into shape (60000,1,28)```

【问题讨论】:

    标签: python python-3.x tensorflow deep-learning artificial-intelligence


    【解决方案1】:

    model.add(tf.keras.layers.Flatten(input_shape=(28,28)))

    它是 28x28 的图像,而不是 32 的矢量 所以我们知道它不应该是 32 的向量 通过留下一个论点

    【讨论】:

      猜你喜欢
      • 1970-01-01
      • 2020-10-13
      • 2017-03-28
      • 1970-01-01
      • 1970-01-01
      • 1970-01-01
      • 2019-02-05
      • 2020-03-14
      • 2016-06-08
      相关资源
      最近更新 更多