我使用下面的代码来计算预测标签和实际标签的熵。数据来自CIFAR-10数据集。
我使用astype（np.float32）将源数据转换为ndarrays，然后在tf.constant（）中使用dtype作为float32。错误消息
TypeError：DataType float32 for attr＆＃39; Tlabels＆＃39;不在允许的列表中 值：int32，int64
列出只允许int32，int64允许的数据类型。如果没有在上面的两个步骤中明确指定数据类型，我在matmul（）操作中面临一个障碍，因为在计算中使用的权重项是浮点数据类型。
f = open('cifar-10-batches-py/data_batch_1', 'rb')
datadict = cPickle.load(f,encoding='bytes')
#f.close()
X = np.asarray(datadict[b"data"]).astype(np.float32) #b prefix is for bytes string literal.
Y = np.asarray(datadict[b'labels']).astype(np.float32)
f = open('cifar-10-batches-py/data_batch_1', 'rb')
datadict = cPickle.load(f,encoding='bytes')
#f.close()
X = np.asarray(datadict[b"data"]).astype(np.float32) #b prefix is for bytes string literal.
Y = np.asarray(datadict[b'labels']).astype(np.float32)
graph = tf.Graph()
with graph.as_default():
tf_train_data = tf.constant(X, dtype = tf.float32)
tf_train_labels = tf.constant(Y, dtype = tf.float32)
tf_test_data = tf.constant(X_test, dtype = tf.float32)
tf_test_labels = tf.constant(Y_test, dtype = tf.float32)
print (tf_train_labels.get_shape())
weights = tf.Variable(tf.truncated_normal([3072, 10]))
print (tf.rank(weights))
biases = tf.Variable(tf.zeros([10]))
logits = tf.matmul(tf_train_data, weights) + biases
print (tf.rank(logits), tf.rank(tf_train_labels), tf.rank(tf_test_labels))
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, tf_train_labels))
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
train_prediction = tf.nn.softmax(logits)
test_prediction = tf.nn.softmax(tf.matmul(tf_test_data, weights) + biases)
这是错误消息
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-74-8e1ffbeb5013> in <module>()
11 logits = tf.matmul(tf_train_data, weights) + biases
12 print (tf.rank(logits), tf.rank(tf_train_labels), tf.rank(tf_test_labels))
---> 13 loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, tf_train_labels))
14 optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
15 train_prediction = tf.nn.softmax(logits)
/Users/ayada/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits(logits, labels, name)
562 if logits.get_shape().ndims == 2:
563 cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
--> 564 precise_logits, labels, name=name)
565 if logits.dtype == dtypes.float16:
566 return math_ops.cast(cost, dtypes.float16)
/Users/ayada/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py in _sparse_softmax_cross_entropy_with_logits(features, labels, name)
1538 """
1539 result = _op_def_lib.apply_op("SparseSoftmaxCrossEntropyWithLogits",
-> 1540 features=features, labels=labels, name=name)
1541 return _SparseSoftmaxCrossEntropyWithLogitsOutput._make(result)
1542
/Users/ayada/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
527 for base_type in base_types:
528 _SatisfiesTypeConstraint(base_type,
--> 529 _Attr(op_def, input_arg.type_attr))
530 attrs[input_arg.type_attr] = attr_value
531 inferred_from[input_arg.type_attr] = input_name
/Users/ayada/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in _SatisfiesTypeConstraint(dtype, attr_def)
58 "DataType %s for attr '%s' not in list of allowed values: %s" %
59 (dtypes.as_dtype(dtype).name, attr_def.name,
---> 60 ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
61
62
TypeError: DataType float32 for attr 'Tlabels' not in list of allowed values: int32, int64
我该如何解决？
答案 0 :(得分：0)
tf.nn.sparse_softmax_cross_entropy_with_logits接受整数类型的稀疏标签。要使用单热浮动标签，请考虑改为使用tf.nn.softmax_cross_entropy_with_logits。