我为我的数据集制作了CNN模型。
我曾使用批次作为Feed数据。 当我使用批量大小是一个，它是工作。 但如果我使用批量大小不是一个（例如：128） 它会犯错误。
这是我的代码。 我附上了我的所有代码。
有1623列数据。
import tensorflow as tf
import numpy as np
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding='SAME')) # l1a shape=(?, 24, 60, 32)
l1 = tf.nn.avg_pool(l1a, ksize=[1, 4, 4, 1],strides=[1, 2, 2, 1], padding='SAME')# l1 shape=(?, 6, 30, 32)
l1 = tf.nn.dropout(l1, p_keep_conv)
l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, strides=[1, 1, 1, 1], padding='SAME')) # l2a shape=(?, 6, 30, 64)
l2 = tf.nn.avg_pool(l2a, ksize=[1, 2, 3, 1], strides=[1, 2, 3, 1], padding='SAME') # l2 shape=(?, 3, 10, 64)
l2 = tf.nn.dropout(l2, p_keep_conv)
l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, strides=[1, 1, 1, 1], padding='SAME')) # l3a shape=(?, 3, 10, 128)
l3 = tf.nn.max_pool(l3a, ksize=[1, 1, 2, 1], strides=[1, 1, 2, 1], padding='SAME') # l3 shape=(?, 3, 5, 128)
l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) # reshape to (?, 1920)
l3 = tf.nn.dropout(l3, p_keep_conv)
l4 = tf.nn.relu(tf.matmul(l3, w4))
l4 = tf.nn.dropout(l4, p_keep_hidden)
pyx = tf.matmul(l4, w_o)
return pyx
X = tf.placeholder(tf.float32, [None, 24,60,1])
Y = tf.placeholder(tf.float32, [None, 1])
w = init_weights([4, 4, 1, 32]) # 4x4x1 conv, 32 outputs
w2 = init_weights([2, 3, 32, 64]) # 2x3x32 conv, 64 outputs
w3 = init_weights([1, 2, 64, 128]) # 1x2x64 conv, 128 outputs
w4 = init_weights([128 * 5 * 3, 625]) # FC 128 * 5 * 3 inputs, 625 outputs
w_o = init_weights([625, 1]) # FC 625 inputs, 1 outputs (labels)
#B = tf.Variable(tf.random_normal([625]))
print ("W shape:", w.get_shape())
print ("W2 shape:", w2.get_shape())
print ("W3 shape:", w3.get_shape())
print ("W4 shape:", w4.get_shape())
print ("Wo shape:", w_o.get_shape())
p_keep_conv = tf.placeholder("float")
p_keep_hidden = tf.placeholder("float")
py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)
squared_deltas1 = tf.square(Y - py_x)
squared_deltas = tf.sqrt(squared_deltas1)
cost = tf.reduce_mean(squared_deltas)
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
cost_sum = tf.summary.scalar("cost",cost)
def read_my_file_format(filename_queue):
reader = tf.TextLineReader(skip_header_lines=1)
_, value = reader.read(filename_queue)
record_defaults = [[1],[1],[1],.........[1],[1],[1]]
#1623
record_defaults = [tf.constant([1], dtype=tf.float32),
tf.constant([1], dtype=tf.float32),
..................
tf.constant([1], dtype=tf.float32),
tf.constant([1], dtype=tf.float32),
]
Col1,Col2,Col3,......,Col1621,Col1622,Col1623=tf.decode_csv(value, record_defaults=record_defaults)
features = tf.pack([Col4,Col5,Col6, ....... Col1618,Col1619,Col1620])
label = tf.pack([Col29])
return features, label
def input_pipeline(batch_size, num_epochs):
min_after_dequeue = 10000
capacity = min_after_dequeue + 3 * batch_size
'''
filename_queue = tf.train.string_input_producer(["G:\CNN\1999.csv","G:\CNN\2000.csv","G:\CNN\2001.csv","G:\CNN\2002.csv",
"G:\CNN\2003.csv","G:\CNN\2004.csv","G:\CNN\2005.csv","G:\CNN\2006.csv",
"G:\CNN\2007.csv","G:\CNN\2008.csv"], num_epochs=num_epochs, shuffle=True)
'''
filename_queue = tf.train.string_input_producer(["test_1000.csv"], num_epochs=num_epochs, shuffle=True)
example, label = read_my_file_format(filename_queue)
example_batch, label_batch = tf.train.shuffle_batch([example, label],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
examples, labels = input_pipeline(128,1)
print (examples)
examples = tf.reshape(examples, [-1,24,60,1])
print (examples)
#examples = examples.reshape(-1, 24, 60, 1) # 28x28x1 input img
i = 0
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess = tf.Session()
merged = tf.summary.merge_all()
trainwriter =tf.summary.FileWriter("./board/custom", sess.graph)
sess.run(init_op)
print(w.eval(session = sess))
print(w2.eval(session = sess))
print(w3.eval(session = sess))
print(w4.eval(session = sess))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
i = i + 1
example_batch, label_batch = sess.run([examples, labels])
sess.run(train_op , feed_dict={X: example_batch, Y: label_batch, p_keep_conv: 0.8, p_keep_hidden: 0.5})
if i % 1 == 0:
summary = sess.run(merged, feed_dict={X: example_batch, Y: label_batch, p_keep_conv: 1, p_keep_hidden: 1})
trainwriter.add_summary(summary,i)
print(cost.eval(feed_dict={X: example_batch, Y: label_batch, p_keep_conv: 1, p_keep_hidden: 1}, session = sess))
'''
loss = tf.abs(y-y_)
accuracy = tf.reduce_mean(loss)
print(cross_entropy.eval(feed_dict={x: example_batch, y_: label_batch}, session = sess))
'''
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
这是选择批量大小的代码。
examples, labels = input_pipeline(128,1)
如果我将批量大小写成大于1，则会出现此错误
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1020 try:
-> 1021 return fn(*args)
1022 except errors.OpError as e:
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1002 feed_dict, fetch_list, target_list,
-> 1003 status, run_metadata)
1004
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
468 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 469 pywrap_tensorflow.TF_GetCode(status))
470 finally:
InvalidArgumentError: Incompatible shapes: [128,1] vs. [256,1]
[[Node: gradients/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/sub_grad/Shape, gradients/sub_grad/Shape_1)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-1-d05205b7cce1> in <module>()
1866 i = i + 1
1867 example_batch, label_batch = sess.run([examples, labels])
-> 1868 sess.run(train_op , feed_dict={X: example_batch, Y: label_batch, p_keep_conv: 0.8, p_keep_hidden: 0.5})
1869
1870 if i % 1 == 0:
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
764 try:
765 result = self._run(None, fetches, feed_dict, options_ptr,
--> 766 run_metadata_ptr)
767 if run_metadata:
768 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
962 if final_fetches or final_targets:
963 results = self._do_run(handle, final_targets, final_fetches,
--> 964 feed_dict_string, options, run_metadata)
965 else:
966 results = []
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1012 if handle is None:
1013 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1014 target_list, options, run_metadata)
1015 else:
1016 return self._do_call(_prun_fn, self._session, handle, feed_dict,
C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1032 except KeyError:
1033 pass
-> 1034 raise type(e)(node_def, op, message)
1035
1036 def _extend_graph(self):
InvalidArgumentError: Incompatible shapes: [128,1] vs. [256,1]
[[Node: gradients/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/sub_grad/Shape, gradients/sub_grad/Shape_1)]]
Caused by op 'gradients/sub_grad/BroadcastGradientArgs', defined at:
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
app.launch_new_instance()
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tornado\ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-d05205b7cce1>", line 51, in <module>
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\training\optimizer.py", line 269, in minimize
grad_loss=grad_loss)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\training\optimizer.py", line 335, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 482, in gradients
in_grads = grad_fn(op, *out_grads)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\math_grad.py", line 594, in _SubGrad
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 390, in _broadcast_gradient_args
name=name)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 759, in apply_op
op_def=op_def)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 1128, in __init__
self._traceback = _extract_stack()
...which was originally created as op 'sub', defined at:
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
[elided 18 identical lines from previous traceback]
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-d05205b7cce1>", line 48, in <module>
squared_deltas1 = tf.square(Y - py_x)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\math_ops.py", line 814, in binary_op_wrapper
return func(x, y, name=name)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 2758, in sub
result = _op_def_lib.apply_op("Sub", x=x, y=y, name=name)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 759, in apply_op
op_def=op_def)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 1128, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Incompatible shapes: [128,1] vs. [256,1]
[[Node: gradients/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/sub_grad/Shape, gradients/sub_grad/Shape_1)]]
我想使用批处理功能但在这种情况下我不能使用它。 我该如何解决这个问题？