如何为tensorflow学习提供批量CSV数据

我有这些数据。 19个数据输入和1个标签

我已经尝试过mnist，tensorboard的例子和csv批处理加载的例子。现在我试图混合这一切。

加载csv数据并将其批量化。只学习1层并检查成本。这就是我想要做的

这是我的代码

import tensorflow as tf 

with tf.name_scope("input") as scope: 
    x = tf.placeholder(tf.float32, [None, 19]) 

with tf.name_scope("weight") as scope: 
    W = tf.Variable(tf.zeros([19, 1])) 

with tf.name_scope("bias") as scope: 
    b = tf.Variable(tf.zeros([1])) 

with tf.name_scope("layer1") as scope: 
    y = tf.nn.relu(tf.matmul(x, W) + b) 

w_hist = tf.summary.histogram("weight", W) 
b_hist = tf.summary.histogram("bias", b) 
y_hist = tf.summary.histogram("y", y) 

with tf.name_scope("y_") as scope:        
    y_ = tf.placeholder(tf.float32, [None, 1]) 

with tf.name_scope("cost") as scope: 
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) 
cost_sum = tf.summary.scalar("cost",cross_entropy) 

with tf.name_scope("train") as scope:  
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 


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], [1], [1], [1], [1],[1], [1], [1], [1], [1],[1], [1], [1], [1], [1]] 

    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), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        tf.constant([1], dtype=tf.float32), 
        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, col4, col5, col6, col7, col8, col9, col10, col11, col12, col13, col14, col15 ,col16, col17, col18, col19, col20 = tf.decode_csv(value, record_defaults=record_defaults)  

    features = tf.pack([col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11, col12, col13, col14, col15 ,col16, col17, col18, col19]) 
    label = tf.pack([col20]) 

    return features, label 


def input_pipeline(batch_size, num_epochs=None): 

    min_after_dequeue = 10000 
    capacity = min_after_dequeue + 3 * batch_size 

    filename_queue = tf.train.string_input_producer(["sampledata1999_2008.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(100,1) 

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) 

sess = tf.Session() 

# Initialize the variables (like the epoch counter).  
sess.run(init_op) 

merged = tf.summary.merge_all() 
trainwriter =tf.summary.FileWriter("./board/custom", sess.graph) 

# Start input enqueue threads. 
coord = tf.train.Coordinator() 
threads = tf.train.start_queue_runners(sess=sess, coord=coord) 

try: 
    i = 0; 
    while not coord.should_stop(): 
     i = i + 1 
     example_batch, label_batch = sess.run([examples, labels]) 
     sess.run(train_step, feed_dict={x: example_batch, y_: label_batch})   
     if i % 100 == 0: 
      summary = sess.run(merged, feed_dict={x: example_batch, y_: label_batch}) 
      trainwriter.add_summary(summary,i)    
      print(example_batch)  

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()

我查批量数据正确送入到cross_entropy。但如果我使用这些

if i % 100 == 0: 
     summary = sess.run(merged, feed_dict={x: example_batch, y_: label_batch}) 
     trainwriter.add_summary(summary,i)    
     print(example_batch)

我已经得到了一个错误信息像这样

--------------------------------------------------------------------------- 
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: Nan in summary histogram for: weight_1 
    [[Node: weight_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](weight_1/tag, weight/Variable/read)]] 

During handling of the above exception, another exception occurred: 

InvalidArgumentError      Traceback (most recent call last) 
<ipython-input-1-d641bed636e8> in <module>() 
    104   example_batch, label_batch = sess.run([examples, labels]) 
    105   sess.run(train_step, feed_dict={x: example_batch, y_: label_batch}) 
--> 106   summary = sess.run(merged, feed_dict={x: example_batch, y_: label_batch}) 
    107   writer.add_summary(summary,i) 
    108   print(example_batch) 

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: Nan in summary histogram for: weight_1 
    [[Node: weight_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](weight_1/tag, weight/Variable/read)]] 

Caused by op 'weight_1', 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-d641bed636e8>", line 17, in <module> 
    w_hist = tf.summary.histogram("weight", W) 
    File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\summary\summary.py", line 205, in histogram 
    tag=scope.rstrip('/'), values=values, name=scope) 
    File "C:\Program Files\Anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 139, in _histogram_summary 
    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): Nan in summary histogram for: weight_1 
    [[Node: weight_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](weight_1/tag, weight/Variable/read)]]

什么是错在我的代码？

来源

2017-02-22 Sunghun Noh