Python 无法将Vgg16与具有cifar10数据的KERA一起使用？

我似乎无法将cifar10数据集与vgg16模型一起使用。。。 出于某种原因，我收到了以下错误消息：

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)]]

---

有一件事可能会出错，那就是输入的大小，因为您定义了网络以拍摄224*224的图像，但为其提供了大小为32*32的图像，但是。。。它是零填充到该大小的，网络只接受最小尺寸为48x48的输入。看起来您正在对输入进行零填充，然后进行打印，但如果我理解正确，则返回使用原始输入？我是零填充。。。输入到vgg16和dense layer进行10-classification。可能会出错的一件事是输入的大小，因为您定义了网络以拍摄224*224的图像，但为其提供了大小为32*32的图像，但是。。。它是零填充到该大小的，网络只接受最小尺寸为48x48的输入。看起来您正在对输入进行零填充，然后进行打印，但如果我理解正确，则返回使用原始输入？我是零填充。。。输入vgg16，致密层进行10级分类。

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()