Tensorflow tf.nn.nce_loss get TypeError： 'Mul'の 'y'を入力すると、引数 'x'の型int32と一致しないfloat型がある

Question

Tensor FlowのBag of Words実装で作業していました。 a

TypeError： 'Mul'の 'y'を入力します。Opは、引数 'x'のint32と一致しないfloat32型です。

tf.nn.nce_loss。私はtf.nn.nce_lossに入るタイプを調べてみましたが、無用に強制しました。どんな助けもありがとう。私はjupyterノートでpythonで作業していました。完全なコードは、データプルを含めている

はhttps://gist.github.com/gammaguy/683a1357fdb044d0abbd897a7179d525

graph = tf.Graph() 

with graph.as_default(): 

    # Input data. 
    train_inputs = tf.placeholder(tf.int32,shape=[batch_size, skip_window * 2],name="train_inputs") 
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1],name="train_labels") 
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32,name="valid_dataset") 

# train_inputs = tf.placeholder(tf.int32,shape=[batch_size, skip_window * 2],name="train_inputs") 
# train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1],name="train_labels") 
# valid_dataset = tf.constant(valid_examples, dtype=tf.int32,name="valid_dataset") 

    with tf.device('/cpu:0'): 
    # Look up embeddings for inputs. 
    embeddings = tf.Variable(
     tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0),name="embeddings") 

    # Embedding size is calculated as shape(train_inputs) + shape(embeddings)[1:] 
    embed = tf.nn.embedding_lookup(embeddings, train_inputs,name="embed") 
    reduced_embed = tf.div(tf.reduce_sum(embed, 1), skip_window*2,name="reduced_embed") 
    # Construct the variables for the NCE loss 
    nce_weights = tf.Variable(
     tf.truncated_normal([vocabulary_size, embedding_size], 
          stddev=1.0/math.sqrt(embedding_size)),name="nce_weights") 
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]),name="nce_biases") 

    print(train_inputs) 
    print(train_labels) 
    print(valid_dataset) 
    print(embeddings) 
    print(embed) 
    print(reduced_embed)  
    print(nce_weights) 
    print(nce_biases) 
    print(num_sampled) 
    print(vocabulary_size) 

    # Compute the average NCE loss for the batch. 
    # tf.nce_loss automatically draws a new sample of the negative labels each 
    # time we evaluate the loss. 
    loss = tf.reduce_mean(
     tf.nn.nce_loss(nce_weights, nce_biases, reduced_embed, train_labels, 
        num_sampled, vocabulary_size)) 

出力

Tensor("train_inputs:0", shape=(128, 2), dtype=int32) 
Tensor("train_labels:0", shape=(128, 1), dtype=int32) 
Tensor("valid_dataset:0", shape=(16,), dtype=int32) 
<tf.Variable 'embeddings:0' shape=(82297, 128) dtype=float32_ref> 
Tensor("embed:0", shape=(128, 2, 128), dtype=float32, device=/device:CPU:0) 
Tensor("reduced_embed:0", shape=(128, 128), dtype=float32, device=/device:CPU:0) 
<tf.Variable 'nce_weights:0' shape=(82297, 128) dtype=float32_ref> 
<tf.Variable 'nce_biases:0' shape=(82297,) dtype=float32_ref> 
64 
82297 
--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords) 
    490     as_ref=input_arg.is_ref, 
--> 491     preferred_dtype=default_dtype) 
    492   except TypeError as err: 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype) 
    703   if ret is None: 
--> 704   ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 
    705 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref) 
    576   "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" 
--> 577   % (dtype.name, t.dtype.name, str(t))) 
    578 return t 

ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("nce_loss/Reshape_1:0", shape=(?, 1, ?), dtype=float32)' 

During handling of the above exception, another exception occurred: 

TypeError         Traceback (most recent call last) 
<ipython-input-7-55d5813d0e24> in <module>() 
    42 loss = tf.reduce_mean(
    43  tf.nn.nce_loss(nce_weights, nce_biases, reduced_embed, train_labels, 
---> 44      num_sampled, vocabulary_size)) 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/ops/nn_impl.py in nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name) 
    1164  remove_accidental_hits=remove_accidental_hits, 
    1165  partition_strategy=partition_strategy, 
-> 1166  name=name) 
    1167 sampled_losses = sigmoid_cross_entropy_with_logits(
    1168  labels=labels, logits=logits, name="sampled_losses") 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/ops/nn_impl.py in _compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, subtract_log_q, remove_accidental_hits, partition_strategy, name) 
    999  row_wise_dots = math_ops.multiply(
    1000   array_ops.expand_dims(inputs, 1), 
-> 1001   array_ops.reshape(true_w, new_true_w_shape)) 
    1002  # We want the row-wise dot plus biases which yields a 
    1003  # [batch_size, num_true] tensor of true_logits. 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in multiply(x, y, name) 
    276 
    277 def multiply(x, y, name=None): 
--> 278 return gen_math_ops._mul(x, y, name) 
    279 
    280 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py in _mul(x, y, name) 
    1432  A `Tensor`. Has the same type as `x`. 
    1433 """ 
-> 1434 result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name) 
    1435 return result 
    1436 

/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords) 
    525     "%s type %s of argument '%s'." % 
    526     (prefix, dtypes.as_dtype(attrs[input_arg.type_attr]).name, 
--> 527     inferred_from[input_arg.type_attr])) 
    528 
    529   types = [values.dtype] 

TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'. 

Answer 1

nce_loss(
    weights, 
    biases, 
    labels, 
    inputs, 
    num_sampled, 
    num_classes, 
    num_true=1, 
    sampled_values=None, 
    remove_accidental_hits=False, 
    partition_strategy='mod', 
    name='nce_loss' 
) 

train_labelsとの交流reduced_embedでご利用いただけます。