我有一个预先训练过的模型，我试图在张量流中进行推理。但我想我错过了一些东西。这是我的架构：
# Define a function to create a conv layer
def add_conv_layer(incoming=None, num_filters=None, kSize=None, pad='SAME', train=False, s=1,
layer_name=None, weights_param=None, bias_param=None, activation_fn=None):
with tf.variable_scope(layer_name):
previous_layer_output_shape = incoming.get_shape().as_list()[-1]
tshape = [kSize, kSize, previous_layer_output_shape, num_filters]
# Set weights for the layer
if weights_param is not None:
weights = tf.Variable(weights_param, name='weights', dtype=tf.float32)
weights = tf.reshape(weights, tshape)
else:
weights = init_weights(tshape)
print weights.shape
# Set bias for the layer
if bias_param is not None:
bias = tf.Variable(bias_param, name='bias', dtype=tf.float32)
else:
bias = init_bias(num_filters)
layer = tf.nn.conv2d(incoming, weights, strides=[1,s,s,1], padding=pad, name='layer_name') + bias
if activation_fn is not None:
layer = activation_fn(layer)
return layer
# Define a function to add a dense layer
def add_dense_layer(input_tensor=None, num_units=None, layer_name=None,
weight_params=None, bias_params=None, activation_fn=None):
with tf.variable_scope(layer_name):
previous_layer_output_shape = input_tensor.get_shape().as_list()[-1]
tshape = [previous_layer_output_shape, num_units]
# Add weights for the layer
if weight_params is not None:
weights = tf.Variable(weight_params, name='weights', dtype=tf.float32)
weights = tf.reshape(weights, tshape)
else:
weights = init_weights(tshape)
# Add bias to the layer
if bias_params is not None:
bias = tf.Variable(bias_params, name='bias', dtype=tf.float32)
else:
bias = init_bias(num_units)
layer = tf.matmul(input_tensor, weights) + bias
if activation_fn is not None:
layer = activation_fn(layer)
return layer
def build_network(features):
features = tf.reshape(features, [-1,224,224,3])
# Conv block 1
conv1_1 = add_conv_layer(features, num_filters=64, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv1_1']['weights'].value,
bias_param=f['conv1_1']['bias'].value,
layer_name='conv1_1')
conv1_2 = add_conv_layer(conv1_1, num_filters=64, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv1_2']['weights'].value,
bias_param=f['conv1_2']['bias'].value,
layer_name='conv1_2')
pool1 = tf.layers.max_pooling2d(inputs=conv1_2,pool_size=(2,2), strides=(2,2), name='pool1')
# Conv block 2
conv2_1 = add_conv_layer(pool1, num_filters=128, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv2_1']['weights'].value,
bias_param=f['conv2_1']['bias'].value,
layer_name='conv2_1')
conv2_2 = add_conv_layer(conv2_1, num_filters=128, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv2_2']['weights'].value,
bias_param=f['conv2_2']['bias'].value,
layer_name='conv2_2')
pool2 = tf.layers.max_pooling2d(inputs=conv2_2,pool_size=(2,2), strides=(2,2), name='pool2')
# Conv block 3
conv3_1 = add_conv_layer(pool2, num_filters=256, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv3_1']['weights'].value,
bias_param=f['conv3_1']['bias'].value,
layer_name='conv3_1')
conv3_2 = add_conv_layer(conv3_1, num_filters=256, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv3_2']['weights'].value,
bias_param=f['conv3_2']['bias'].value,
layer_name='conv3_2')
conv3_3 = add_conv_layer(conv3_2, num_filters=256, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv3_3']['weights'].value,
bias_param=f['conv3_3']['bias'].value,
layer_name='conv3_3')
pool3 = tf.layers.max_pooling2d(inputs=conv3_3,pool_size=(2,2), strides=(2,2), name='pool3')
# Conv block 4
conv4_1 = add_conv_layer(pool3, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv4_1']['weights'].value,
bias_param=f['conv4_1']['bias'].value,
layer_name='conv4_1')
conv4_2 = add_conv_layer(conv4_1, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv4_2']['weights'].value,
bias_param=f['conv4_2']['bias'].value,
layer_name='conv4_2')
conv4_3 = add_conv_layer(conv4_2, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv4_3']['weights'].value,
bias_param=f['conv4_3']['bias'].value,
layer_name='conv4_3')
pool4 = tf.layers.max_pooling2d(inputs=conv4_3,pool_size=(2,2), strides=(2,2), name='pool4')
# Conv block 5
conv5_1 = add_conv_layer(pool4, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv5_1']['weights'].value,
bias_param=f['conv5_1']['bias'].value,
layer_name='conv5_1')
conv5_2 = add_conv_layer(conv5_1, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv5_2']['weights'].value,
bias_param=f['conv5_2']['bias'].value,
layer_name='conv5_2')
conv5_3 = add_conv_layer(conv5_2, num_filters=512, kSize=3, activation_fn=tf.nn.relu,
weights_param=f['conv5_3']['weights'].value,
bias_param=f['conv5_3']['bias'].value,
layer_name='conv5_3')
pool5 = tf.layers.max_pooling2d(inputs=conv5_3, pool_size=(7,7),strides=(7,7), name='img1_pool5')
flatten_layer = tf.contrib.layers.flatten(inputs=pool5)
fc6 = add_dense_layer(flatten_layer, num_units=1024, activation_fn=tf.nn.relu,
weight_params=f['fc6']['weights'].value,
bias_params= f['fc6']['bias'].value,
layer_name='fc6')
fc7 = add_dense_layer(fc6, num_units=256, activation_fn=tf.nn.relu,
weight_params=f['fc7']['weights'].value,
bias_params= f['fc7']['bias'].value,
layer_name='fc7')
output = add_dense_layer(fc7, num_units=2, activation_fn=tf.nn.softmax,
weight_params=f['out']['weights'].value,
bias_params= f['out']['bias'].value,
layer_name='output')
return output
现在，我想运行build_network函数的新输入并进行推理。我这样做是这样的（我觉得这是错误的）：
init = tf.global_variables_initializer()
out = build_network(features)
with tf.Session() as sess:
sess.run(init)
print(out.eval())
但是有了这个，我得到每个输入相同的输出。请帮忙
答案 0 :(得分：0)
从原始编辑：您的 build_network函数仅创建图表。 sess.run(init)行使用每个权重的默认初始值设定项初始化网络权重，即该行未训练后的网络。
在向代码添加te add_*_layer函数的实现后，我现在看到你确实初始化了权重。
此答案的评论中的进一步信息表明，错误可能在于权重从Theano转换为Tensorflow，或者如@Sebastian所建议的那样，数据布局的差异。
但是，如果没有进一步的信息，我很难肯定地说出更多信息。