【发布时间】：2016-10-04 09:14:48
【问题描述】：
我一直在尝试运行从here 获得的下面的代码，尽管我几乎没有更改图像大小（350,350 而不是 150、150），但仍然无法使其正常工作。我得到了我理解的上述过滤器错误（标题中），但我没有做错，所以我不明白这一点。它基本上说我不能有比输入更多的节点，对吗？
我最终能够通过更改这一行来破解我的解决方案：
model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(3, IMG_WIDTH, IMG_HEIGHT)))
用这个：
model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
但我仍然想了解为什么会这样。
这是下面的代码以及我遇到的错误。不胜感激（我使用的是 Python Anaconda 2.7.11）。
# IMPORT LIBRARIES --------------------------------------------------------------------------------#
import glob
import tensorflow
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from settings import RAW_DATA_ROOT
# GLOBAL VARIABLES --------------------------------------------------------------------------------#
TRAIN_PATH = RAW_DATA_ROOT + "/train/"
TEST_PATH = RAW_DATA_ROOT + "/test/"
IMG_WIDTH, IMG_HEIGHT = 350, 350
NB_TRAIN_SAMPLES = len(glob.glob(TRAIN_PATH + "*"))
NB_VALIDATION_SAMPLES = len(glob.glob(TEST_PATH + "*"))
NB_EPOCH = 50
# FUNCTIONS ---------------------------------------------------------------------------------------#
def baseline_model():
"""
The Keras library provides wrapper classes to allow you to use neural network models developed
with Keras in scikit-learn. The code snippet below is used to construct a simple stack of 3
convolution layers with a ReLU activation and followed by max-pooling layers. This is very
similar to the architectures that Yann LeCun advocated in the 1990s for image classification
(with the exception of ReLU).
:return: The training model.
"""
model = Sequential()
model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(3, IMG_WIDTH, IMG_HEIGHT)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 5, 5, border_mode='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 5, 5, border_mode='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# Add a fully connected layer layer that converts our 3D feature maps to 1D feature vectors
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
# Use a dropout layer to reduce over-fitting, by preventing a layer from seeing twice the exact
# same pattern (works by switching off a node once in a while in different epochs...). This
# will also serve as out output layer.
model.add(Dropout(0.5))
model.add(Dense(8))
model.add(Activation('softmax'))
# Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def train_model(model):
"""
Simple script that uses the baseline model and returns a trained model.
:param model: model
:return: model
"""
# Define the augmentation configuration we will use for training
TRAIN_DATAGEN = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# Build the train generator
TRAIN_GENERATOR = TRAIN_DATAGEN.flow_from_directory(
TRAIN_PATH,
target_size=(IMG_WIDTH, IMG_HEIGHT),
batch_size=32,
class_mode='categorical')
TEST_DATAGEN = ImageDataGenerator(rescale=1. / 255)
# Build the validation generator
TEST_GENERATOR = TEST_DATAGEN.flow_from_directory(
TEST_PATH,
target_size=(IMG_WIDTH, IMG_HEIGHT),
batch_size=32,
class_mode='categorical')
# Train model
model.fit_generator(
TRAIN_GENERATOR,
samples_per_epoch=NB_TRAIN_SAMPLES,
nb_epoch=NB_EPOCH,
validation_data=TEST_GENERATOR,
nb_val_samples=NB_VALIDATION_SAMPLES)
# Always save your weights after training or during training
model.save_weights('first_try.h5')
# END OF FILE -------------------------------------------------------------------------------------#
和错误：
Using TensorFlow backend.
Training set: 0 files.
Test set: 0 files.
Traceback (most recent call last):
File "/Users/christoshadjinikolis/GitHub_repos/datareplyuk/ODSC_Facial_Sentiment_Analysis/src/model/__init__.py", line 79, in <module>
model = baseline_model()
File "/Users/christoshadjinikolis/GitHub_repos/datareplyuk/ODSC_Facial_Sentiment_Analysis/src/model/training_module.py", line 31, in baseline_model
model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(3, IMG_WIDTH, IMG_HEIGHT)))
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/models.py", line 276, in add
layer.create_input_layer(batch_input_shape, input_dtype)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/engine/topology.py", line 370, in create_input_layer
self(x)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/engine/topology.py", line 514, in __call__
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/engine/topology.py", line 572, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-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 "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/layers/convolutional.py", line 466, in call
filter_shape=self.W_shape)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 1579, in conv2d
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 394, in conv2d
data_format=data_format, name=name)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op
op_def=op_def)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2319, in create_op
set_shapes_for_outputs(ret)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1711, in set_shapes_for_outputs
shapes = shape_func(op)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 246, in conv2d_shape
padding)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 184, in get2d_conv_output_size
(row_stride, col_stride), padding_type)
File "/Users/christoshadjinikolis/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 149, in get_conv_output_size
"Filter: %r Input: %r" % (filter_size, input_size))
ValueError: Filter must not be larger than the input: Filter: (5, 5) Input: (3, 350)
【问题讨论】：
-
Tensorflow 通常使用 NHWC 格式，这意味着形状被指定为 (batch_size, height, width, channels)。快速浏览一下 keras 文档 (keras.io/getting-started/sequential-model-guide)，keras 的一个选项是将形状分别指定为（通道、高度、宽度）和 batch_size，在您的示例中也是如此。所以看起来你的例子是正确的，应该有效，而且修复没有意义。如果我是你，我会使用 pdb 单步执行调用堆栈，以找出错误的形状从 keras 传递到 tensorflow 的位置。
-
谢谢，下周晚些时候我会看看并发布我的发现。
-
另一种可能性是该示例适用于 Tensorflow 以外的某些框架，并且该框架指定了带有顺序的形状（通道、高度、宽度）。对于 Tensorflow，您可能确实需要更改顺序。但这也让我感到困惑，因为我认为 keras 应该可以跨不同的机器学习框架移植。
-
查看更多：keras.io/backend您可以在此页面中搜索[batch, channels, height, width]。现在看起来确实需要为 Tensorflow 更改形状顺序，而 keras 似乎并没有在内部/自动处理它。顺便说一句，我对 keras 知之甚少；这里的一些 keras 专家肯定可以提供更好的建议。
标签： python-2.7 tensorflow convolution keras