tf.nn，tf.layers， tf.contrib模組有很多功能是重複的

下面是對三個模組的簡述：

• tf.nn ：提供神經網路相關操作的支援，包括卷積操作（conv）、池化操作（pooling）、歸一化、loss、分類操作、embedding、RNN、Evaluation。
• tf.layers：主要提供的高層的神經網路，主要和卷積相關的，tf.nn會更底層一些。
• tf.contrib：tf.contrib.layers提供夠將計算圖中的 網路層、正則化、摘要操作、是構建計算圖的高階操作，但是tf.contrib包含不穩定和實驗程式碼，有可能以後API會改變。

TensorFlow中文社群提供了詳細的介紹：

• Activation Functions
  
  • tf.nn.relu(features, name=None)
  • tf.nn.relu6(features, name=None)
  • tf.nn.softplus(features, name=None)
  • tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
  • tf.nn.bias_add(value, bias, name=None)
  • tf.sigmoid(x, name=None)
  • tf.tanh(x, name=None)
• Convolution
  
  • tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
  • tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)
  • tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)
• Pooling
  
  • tf.nn.avg_pool(value, ksize, strides, padding, name=None)
  • tf.nn.max_pool(value, ksize, strides, padding, name=None)
  • tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)
• Normalization
  
  • tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)
  • tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)
  • tf.nn.moments(x, axes, name=None)
• Losses
  
  • tf.nn.l2_loss(t, name=None)
• Classification
  
  • tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)
  • tf.nn.softmax(logits, name=None)
  • tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None)
• Embeddings
  
  • tf.nn.embedding_lookup(params, ids, name=None)

• Evaluation

  • tf.nn.top_k(input, k, name=None)
  • tf.nn.in_top_k(predictions, targets, k, name=None)

• Candidate Sampling（包含Sampled Loss Functions和Miscellaneous candidate sampling utilities）

• Sampled Loss Functions

  • tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, name=’nce_loss’)
  • tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, name=’sampled_softmax_loss’)
• Candidate Samplers
  
  • tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)
  • tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)
  • tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)
  • tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file=”, distortion=0.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=[], seed=None, name=None)
• Miscellaneous candidate sampling utilities
  
  • tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)