[英]Incompatible shapes in Keras when loading a model with custom layer
我正在嘗試在Keras中實現一個Subpixel upconvolution層。 我可以毫無問題地訓練模型並保存它。 但是我無法加載那個模型。 我總是得到尺寸錯誤的錯誤。
它的唯一工作方式是保存權重,創建新模型,然后加載權重。 但是,這並不理想,因為優化器會重置,因此很難恢復訓練。
import keras
import numpy as np
import tensorflow as tf
class Subpixel(keras.layers.Conv2D):
def __init__(self,
filters,
kernel_size,
scale,
padding='valid',
data_format='channels_last',
strides=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='he_normal',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super().__init__(
filters=scale * scale * filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.scale = scale
self.data_format = data_format
def call(self, inputs):
return tf.depth_to_space(super().call(inputs), self.scale)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
b, k, r, c = super().compute_output_shape(input_shape)
return b, k // (self.scale ** 2), r * self.scale, c * self.scale
else:
b, r, c, k = super().compute_output_shape(input_shape)
return b, r * self.scale, c * self.scale, k // (self.scale ** 2)
def get_config(self):
config = super(keras.layers.Conv2D, self).get_config()
config['filters'] = int(config['filters'] / self.scale * self.scale)
config['scale'] = self.scale
return config
X = np.random.rand(100, 2, 2, 1)
y = np.random.rand(100, 4, 4, 1)
inputs = keras.layers.Input(shape=(2, 2, 1))
x = Subpixel(4, 4, 2, padding='same')(inputs)
output = keras.layers.Dense(1, activation='sigmoid')(x)
model = keras.models.Model(inputs, output)
model.compile(optimizer='sgd',
loss='mean_absolute_error',
metrics=[])
model.fit(X, y)
model.save('foo.h5')
foo = keras.models.load_model('foo.h5', custom_objects={'Subpixel': Subpixel})
似乎沖突是在權重文件中的形狀和加載的體系結構之間。內核形狀在加載的模型上是不正確的。 當它應該是4,4,1,16時,它是4,4,1,64。 輸出如下:
self = TensorShape([Dimension(4), Dimension(4), Dimension(1), Dimension(64)])
other = TensorShape([Dimension(4), Dimension(4), Dimension(1), Dimension(16)])
def assert_is_compatible_with(self, other):
"""Raises exception if `self` and `other` do not represent the same shape.
This method can be used to assert that there exists a shape that both
`self` and `other` represent.
Args:
other: Another TensorShape.
Raises:
ValueError: If `self` and `other` do not represent the same shape.
"""
if not self.is_compatible_with(other):
> raise ValueError("Shapes %s and %s are incompatible" % (self, other))
E ValueError: Shapes (4, 4, 1, 64) and (4, 4, 1, 16) are incompatible
非常愚蠢的錯誤。 這條線:
config['filters'] = int(config['filters'] / self.scale * self.scale)
應該:
config['filters'] = int(config['filters'] / (self.scale * self.scale))
否則,在序列化圖層時,會保存過濾器的錯誤輸入參數。 基本上我被運算符優先級混淆了。
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