Tensorflow 的tf.one_hot()功能:dense to one hot

本篇博客详细介绍了Tensorflow中的tf.one_hot()函数,用于将稠密编码的数据转换为独热编码。文章涵盖了函数的参数设置,包括indices、depth、on_value、off_value、axis和dtype,并强调了数据类型匹配的重要性。通过该函数,可以在指定轴上创建一个独热维度的张量,这对于分类问题尤其有用。

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import tensorflow as tf
indices = [[3], [5], [0], [7]]
indices = tf.concat(0, indices)
indices = tf.reshape(indice, (4, 1))
a = tf.one_hot(indices, depth=10, on_value=None, off_value=None, axis=None, dtype=None, name=None)
print ("a is : ")
print a
b = tf.reshape(a, (4, 10))
print ("a is : ")
print b

'''
a is : 
Tensor("one_hot:0", shape=(4, 1, 10), dtype=float32)
a is : 
Tensor("Reshape_1:0", shape=(4, 10), dtype=float32)
'''
Args:
  • indices: A Tensor of indices.
  • depth: A scalar defining the depth of the one hot dimension.
  • on_value: A scalar defining the value to fill in output when indices[j] = i. (default: 1)
  • off_value: A scalar defining the value to fill in output when indices[j] != i. (default: 0)
  • axis: The axis to fill (default: -1, a new inner-most axis).
  • dtype: The data type of the output tensor.
Returns:
  • output: The one-hot tensor.
Raises:
  • TypeError: If dtype of either on_value or off_value don't match dtype
  • TypeError: If dtype of on_value and off_value don't match one another

``` !mkdir -p ~/.keras/datasets !cp work/mnist.npz ~/.keras/datasets/ import warnings warnings.filterwarnings("ignore") from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() print(f"训练数据形状: {train_images.shape}") print(f"训练标签长度: {len(train_labels)}") print(f"测试数据形状: {test_images.shape}") print(f"测试标签长度: {len(test_labels)}") from keras import models from keras import layers # 构建神经网络模型 network = models.Sequential() network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) # 隐藏层:512个神经元,激活函数为ReLU network.add(layers.Dense(10, activation='softmax')) # 输出层:10个分类,激活函数为Softmax # 编译模型 network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) # 数据预处理 train_images = train_images.reshape((60000, 28 * 28)) # 将图像展平成一维向量 train_images = train_images.astype('float32') / 255 # 归一化到[0,1] test_images = test_images.reshape((10000, 28 * 28)) test_images = test_images.astype('float32') / 255 # 标签编码 from keras.utils import to_categorical train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels) # 训练模型 network.fit(train_images, train_labels, epochs=5, batch_size=128) # 测试模型性能 test_loss, test_acc = network.evaluate(test_images, test_labels) print('Test accuracy:', test_acc)```W0402 08:09:22.415642 140410418362176 deprecation.py:323] From /opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0402 08:09:22.484165 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead. W0402 08:09:22.495126 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead. W0402 08:09:22.537523 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. W0402 08:09:22.546429 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead. W0402 08:09:22.548026 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead. W0402 08:09:22.566734 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. W0402 08:09:22.567799 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead. W0402 08:09:22.613820 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
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04-03
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