我似乎无法将cifar10数据集与vgg16模型一起使用... 出于某种原因，我收到此错误消息：
Traceback (most recent call last):
File "keras_cnn_vgg16_mnnist.py", line 102, in <module>
fws()
File "keras_cnn_vgg16_mnnist.py", line 96, in fws
validation_data=(x_test[:10], y_test[:10]))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1501, in fit
initial_epoch=initial_epoch)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1155, in _fit_loop
outs = f(ins_batch)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 2231, in __call__
feed_dict=feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Matrix size-incompatible: In[0]: [128,512], In[1]: [25088,4096]
[[Node: vgg16/fc1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](vgg16/flatten/Reshape, fc1/kernel/read)]]
Caused by op u'vgg16/fc1/MatMul', defined at:
File "keras_cnn_vgg16_mnnist.py", line 102, in <module>
fws()
File "keras_cnn_vgg16_mnnist.py", line 79, in fws
model_output = model(input)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 585, in __call__
output = self.call(inputs, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2027, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2178, in run_internal_graph
output_tensors = _to_list(layer.call(computed_tensor, **kwargs))
File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 840, in call
output = K.dot(inputs, self.kernel)
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 936, in dot
out = tf.matmul(x, y)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1801, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1263, in _mat_mul
transpose_b=transpose_b, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Matrix size-incompatible: In[0]: [128,512], In[1]: [25088,4096]
[[Node: vgg16/fc1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](vgg16/flatten/Reshape, fc1/kernel/read)]]
坦率地说，没有意义，因为我使用keras，使用tensorflow作为后端..
我不确定如何解释此错误消息？
这是代码：
from keras.utils import np_utils
from keras import metrics
import keras
from keras import backend as K
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape
from keras.models import Model
from keras.layers import Input, Dense
import tensorflow as tf
from keras.datasets import mnist,cifar10
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 32, 32
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
#print('x_train shape:', x_train.shape)
#print(x_train.shape[0], 'train samples')
#print(x_test.shape[0], 'test samples')
if K.image_data_format() == 'channels_first':
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)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 1)
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 = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
def fws():
#print "Inside"
# Params:
# batch , lr, decay , momentum, epochs
#
#Input shape: (batch_size,40,45,3)
#output shape: (1,15,50)
# number of unit in conv_feature_map = splitd
input = Input(shape=(img_rows,img_cols,3))
zero_padded_section = keras.layers.convolutional.ZeroPadding2D(padding=(96,96), data_format='channels_last')(input)
print zero_padded_section
model = keras.applications.vgg16.VGG16(include_top = True,
weights = 'imagenet',
input_shape = (224,224,3),
pooling = 'max',
classes = 1000)
model_output = model(input)
#FC
dense1 = Dense(units = 512, activation = 'relu', name = "dense_1")(model_output)
dense2 = Dense(units = 256, activation = 'relu', name = "dense_2")(dense1)
dense3 = Dense(units = 10 , activation = 'softmax', name = "dense_3")(dense2)
model = Model(inputs = input , outputs = dense3)
#sgd = SGD(lr=0.08,decay=0.025,momentum = 0.99,nesterov = True)
model.compile(loss="categorical_crossentropy", optimizer='adam' , metrics = [metrics.categorical_accuracy])
model.fit(x_train[:500], y_train[:500],
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test[:10], y_test[:10]))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
fws()