问题描述
我在 Keras ="noreferrer"> 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，说明问题是Tensorflow版本的问题，所以我将Tensorflow升级到了0.12.0，但仍然存在，是网络问题还是我遗漏了某些东西，input_shape应该是什么样?
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.
来自 Keras图像处理文档:
Keras将卷积操作映射到选定的后端(theano或张量流).但是，两个后端对于尺寸的排序都做出了不同的选择.如果您的图像批次是具有C通道的HxW大小的N张图像，则theano使用NCHW排序，而张量流使用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允许您选择自己喜欢的顺序，并将进行转换以映射到后面的后端.但是，如果选择image_ordering_dim="th"，则期望使用Theano样式排序(NCHW，您的代码中包含的那个)；如果选择image_ordering_dim="tf"，则期望使用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"，因此，如果将数据重塑为张量流样式，则它应该可以工作:
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)
这篇关于为"Conv2D"从1中减去3导致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！