Question

在使用Conv2D和其他层之前，我想在第一层中调整图像的形状并调整其大小。输入将是一个展平的数组。这是我的代码：

#Create flat example image:
img_test = np.zeros((120,160))
img_test_flat = img_test.flatten()

reshape_model = Sequential()
reshape_model.add(tf.keras.layers.InputLayer(input_shape=(img_test_flat.shape)))
reshape_model.add(tf.keras.layers.Reshape((120, 160,1)))
reshape_model.add(tf.keras.layers.experimental.preprocessing.Resizing(28, 28, interpolation='nearest'))

result = reshape_model(img_test_flat)
result.shape

不幸的是，这段代码导致我在下面添加的错误。问题是什么？如何正确调整扁平数组的大小和大小？

    WARNING:tensorflow:Model was constructed with shape (None, 19200) for input Tensor("input_13:0", shape=(None, 19200), dtype=float32), but it was called on an input with incompatible shape (19200,).

InvalidArgumentError: Input to reshape is a tensor with 19200 values, but the requested shape has 368640000 [Op:Reshape]

编辑：我尝试过：

reshape_model = Sequential()
reshape_model.add(tf.keras.layers.InputLayer(input_shape=(None, img_test_flat.shape[0])))
reshape_model.add(tf.keras.layers.Reshape((120, 160,1)))
reshape_model.add(tf.keras.layers.experimental.preprocessing.Resizing(28, 28, interpolation='nearest'))

哪位给了我

WARNING:tensorflow:Model was constructed with shape (None, None, 19200) for input Tensor("input_19:0", shape=(None, None, 19200), dtype=float32), but it was called on an input with incompatible shape (19200,).

EDIT2：我从一维数组中接收C ++中的输入，并将其传递给

  // Copy value to input buffer (tensor)
  for (size_t i = 0; i < fb->len; i++){
    model_input->data.i32[i] = (int32_t) (fb->buf[i]);

所以我传递给模型的是平面数组。

Answer 1

您对形状的使用在这里根本没有意义。输入的第一维应为样本数。应该是19,200还是1个样本？

input_shape应该省略样本数，因此，如果要1个样本，输入形状应为19,200。如果您有19,200个样本，则形状应为1。

重塑层也省略了样本数量，因此Keras感到困惑。您到底想做什么？

这似乎是您要实现的目标，但我个人将调整神经网络之外图像的大小：

import numpy as np
import tensorflow as tf

img_test = np.zeros((120,160)).astype(np.float32)
img_test_flat = img_test.reshape(1, -1)

reshape_model = tf.keras.Sequential()
reshape_model.add(tf.keras.layers.InputLayer(input_shape=(img_test_flat.shape[1:])))
reshape_model.add(tf.keras.layers.Reshape((120, 160,1)))
reshape_model.add(tf.keras.layers.Lambda(lambda x: tf.image.resize(x, (28, 28))))

result = reshape_model(img_test_flat)

print(result.shape)

TensorShape([1, 28, 28, 1])

可以随意使用Resizing层而不是Lambda层，由于我的Tensorflow版本，我不能使用它。

张量流重塑和调整大小层的错误

1 个答案: