I was working on a Bag of Words implementation in Tensor Flow and got a
TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'.
in the tf.nn.nce_loss. I tried looking at the types going into tf.nn.nce_loss and tried coercing them to no avail. Any help would be appreciated. I was working in python in a jupyter notebook.
The full code is including data pull is available at 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)) Output
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'.