我正在Tensorflow框架中为Conv神经网络实现NDCG成本函数。输入数据是形状为（1499668，6，15，1）的数组，目标值是形状（1499668，6）
# log 2 with tensorflow
def log2(x):
num = tf.log(x)
den = tf.log(tf.constant(2,dtype=num.dtype))
return num/den
#Create input placeholders
x = tf.placeholder("float", shape=[None, 6,15,1])
y = tf.placeholder("float", shape=[None, n_classes])
target_logs = tf.placeholder("float",shape=[None, n_classes])
#Define convolutional layer
def conv2d(x, W, b, strides=1, reuse=True):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
#Define Maxpool layer
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
#Define a convolutional neural network function
def conv_net(x, weights, biases):
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
conv1 = maxpool2d(conv1, k=2)
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
conv2 = maxpool2d(conv2, k=2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
conv3 = maxpool2d(conv3, k=2)
conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
conv4 = maxpool2d(conv4, k=2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv4, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Output, class prediction
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
#Define Loss and Activation functions
pred = conv_net(x, weights, biases)
print(pred.shape)
# Get the indices of sorted predictions
sort_op,sort_indices = tf.nn.top_k(pred,k=6)
sort_op_act,sort_indices_act = tf.nn.top_k(y,k=6)
# Applying the sorting to log values
sort_log_vals = tf.gather(target_logs,sort_indices)
dcg_cost_a = tf.reduce_sum(tf.squared_difference(tf.reduce_sum(tf.divide(y,sort_log_vals),0),tf.reduce_sum(tf.divide(sort_op_act,target_logs),0)))
dcg_cost = tf.reduce_sum(tf.squared_difference(tf.reduce_sum(tf.divide(sort_op,target_logs),0),tf.reduce_sum(tf.divide(sort_op_act,target_logs),0)))
cr_ent_cost_sg = tf.losses.sigmoid_cross_entropy(y,pred)
tot_cost = dcg_cost + cr_ent_cost_sg + dcg_cost_a
#cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=y))
#optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(tot_cost)
Optimizer = tf.train.AdamOptimizer()
optim = Optimizer.minimize(loss=dcg_cost)
optim2 = Optimizer.minimize(loss=cr_ent_cost_sg)
optim3 = Optimizer.minimize(loss=tot_cost)
#Evaluate Model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
#calculate accuracy across all the given data and average them out.
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#Train and Test the Model
with tf.Session() as sess:
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
for batch in range(len(train_np)//batch_size):
batch_x = train_np[batch*batch_size:min((batch+1)*batch_size,len(train_np))]
batch_y = train_np_y[batch*batch_size:min((batch+1)*batch_size,len(train_np_y))]
print(batch_y.shape)
print(batch_x.shape)
# Run optimization op and Calculate batch loss and accuracy
##opt = sess.run(optimizer, feed_dict={x: batch_x,
## y: batch_y})
num_rows, num_cols = batch_y.shape
y_logs = np.asarray([np.log2(np.arange(2,8)) for itr in range(0,num_rows)])
print(y_logs.shape)
v1,lv = sess.run([optim,dcg_cost],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
print(lv)
v2,lv2 = sess.run([optim2,cr_ent_cost_sg],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
print(lv2)
v3,lv3 = sess.run([optim3,tot_cost],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
print (lv+lv2,lv3)
输出如下
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1321 try:
-> 1322 return fn(*args)
1323 except errors.OpError as e:
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1306 return self._call_tf_sessionrun(
-> 1307 options, feed_dict, fetch_list, target_list, run_metadata)
1308
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1408 self._session, options, feed_dict, fetch_list, target_list,
-> 1409 run_metadata)
1410 else:
InvalidArgumentError: Incompatible shapes: [10000,6] vs. [10000,6,6]
[[Node: truediv_44 = RealDiv[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_2_0_1, GatherV2_11)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-85-33963d934f68> in <module>()
26 v2,lv2 = sess.run([optim2,cr_ent_cost_sg],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
27 print(lv2)
---> 28 v3,lv3 = sess.run([optim3,tot_cost],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
29 print (lv+lv2,lv3)
30
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1133 if final_fetches or final_targets or (handle and feed_dict_tensor):
1134 results = self._do_run(handle, final_targets, final_fetches,
-> 1135 feed_dict_tensor, options, run_metadata)
1136 else:
1137 results = []
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1314 if handle is None:
1315 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1316 run_metadata)
1317 else:
1318 return self._do_call(_prun_fn, handle, feeds, fetches)
/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1333 except KeyError:
1334 pass
-> 1335 raise type(e)(node_def, op, message)
1336
1337 def _extend_graph(self):
InvalidArgumentError: Incompatible shapes: [10000,6] vs. [10000,6,6]
[[Node: truediv_44 = RealDiv[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_2_0_1, GatherV2_11)]]
Caused by op 'truediv_44', defined at:
File "/usr/local/anaconda/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/anaconda/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/anaconda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/anaconda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/anaconda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/anaconda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/anaconda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/anaconda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/anaconda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/anaconda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2802, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-72-9bbef16c3cc3>", line 13, in <module>
dcg_cost_a = tf.reduce_sum(tf.squared_difference(tf.reduce_sum(tf.divide(y,sort_log_vals),0),tf.reduce_sum(tf.divide(sort_op_act,target_logs),0)))
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 198, in divide
return x / y
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 847, in binary_op_wrapper
return func(x, y, name=name)
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 955, in _truediv_python3
return gen_math_ops.real_div(x, y, name=name)
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 5704, in real_div
"RealDiv", x=x, y=y, name=name)
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
op_def=op_def)
File "/usr/local/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1740, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Incompatible shapes: [10000,6] vs. [10000,6,6]
[[Node: truediv_44 = RealDiv[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_2_0_1, GatherV2_11)]]
该脚本错误位于：v3,lv3 = sess.run([optim3,tot_cost],feed_dict={x:batch_x,y:batch_y,target_logs:y_logs})
以前的计算（v1，lv1和v2，lv2）在相同的字典输入下也可以正常工作。不太确定是什么导致这种形状不兼容。感谢任何输入。