问题描述
我将 Keras 与 Tensorflow 作为后端，这是我的代码:
I'm using Keras with Tensorflow as backend , here is my code:
import numpy as np
np.random.seed(1373)
import tensorflow as tf
tf.python.control_flow_ops = tf
import os
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
batch_size = 128
nb_classes = 10
nb_epoch = 12
img_rows, img_cols = 28, 28
nb_filters = 32
nb_pool = 2
nb_conv = 3
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape[0])
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=(1, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=["accuracy"])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
和引用错误:
Using TensorFlow backend.
60000
('X_train shape:', (60000, 1, 28, 28))
(60000, 'train samples')
(10000, 'test samples')
Traceback (most recent call last):
File "mnist.py", line 154, in <module>
input_shape=(1, img_rows, img_cols)))
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 276, in add
layer.create_input_layer(batch_input_shape, input_dtype)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 370, in create_input_layer
self(x)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 514, in __call__
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 572, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 149, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
File "/usr/local/lib/python2.7/dist-packages/keras/layers/convolutional.py", line 466, in call
filter_shape=self.W_shape)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 1579, in conv2d
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 396, in conv2d
data_format=data_format, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2242, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1617, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1568, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.py", line 675, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Negative dimension size caused by subtracting 3 from 1 for 'Conv2D' (op: 'Conv2D') with input shapes: [?,1,28,28], [3,3,28,32].
首先我看到一些答案是 Tensorflow 版本的问题，所以我将 Tensorflow 升级到 0.12.0，但仍然存在，是网络问题或者我遗漏了什么，input_shape 应该是什么样的?
First I saw some answers that problem is with Tensorflow version so I upgrade Tensorflow to 0.12.0, but still exist , is that problem with network or I missing something, what should input_shape looks like?
更新这是./keras/keras.json:
{
"image_dim_ordering": "tf",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
推荐答案
您的问题来自 keras.json 中的 image_ordering_dim.
Your issue comes from the image_ordering_dim in keras.json.
来自 Keras 图像处理文档:
dim_ordering:{"th", "tf"} 之一.tf"模式意味着图像应该具有形状(样本、高度、宽度、通道)，th"模式意味着图像应该具有形状(样本、通道、高度、宽度).它默认为在 ~/.keras/keras.json 的 Keras 配置文件中找到的 image_dim_ordering 值.如果你从来没有设置它，那么它会是tf".
Keras 将卷积操作映射到选定的后端(theano 或 tensorflow).但是，两个后端对维度的排序做出了不同的选择.如果您的图像批次是具有 C 通道的 HxW 大小的 N 个图像，则 theano 使用 NCHW 排序，而 tensorflow 使用 NHWC 排序.
Keras maps the convolution operation to the chosen backend (theano or tensorflow). However, both backends have made different choices for the ordering of the dimensions. If your image batch is of N images of HxW size with C channels, theano uses the NCHW ordering while tensorflow uses the NHWC ordering.
Keras 允许您选择您喜欢的顺序，并将进行转换以映射到后面的后端.但是如果你选择 image_ordering_dim="th" 它需要 Theano 风格的排序(NCHW，你的代码中的那个)，如果 image_ordering_dim="tf" 它需要 tensorflow样式排序 (NHWC).
Keras allows you to choose which ordering you prefer and will do the conversion to map to the backends behind. But if you choose image_ordering_dim="th" it expects Theano-style ordering (NCHW, the one you have in your code) and if image_ordering_dim="tf" it expects tensorflow-style ordering (NHWC).
由于您的 image_ordering_dim 设置为 "tf"，如果您将数据重塑为 tensorflow 样式，它应该可以工作:
Since your image_ordering_dim is set to "tf", if you reshape your data to the tensorflow style it should work:
X_train = X_train.reshape(X_train.shape[0], img_cols, img_rows, 1)
X_test = X_test.reshape(X_test.shape[0], img_cols, img_rows, 1)
和
input_shape=(img_cols, img_rows, 1)
这篇关于'Conv2D' 由 1 减 3 引起的负尺寸大小的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！