这篇教程Python layers.Softmax方法代码示例写得很实用，希望能帮到您。
本文整理汇总了Python中keras.layers.Softmax方法的典型用法代码示例。如果您正苦于以下问题：Python layers.Softmax方法的具体用法？Python layers.Softmax怎么用？Python layers.Softmax使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.Softmax方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: __output# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def __output(self, dec_output): output_dropout_layer = Dropout(self.output_dropout) output_layer = Conv1D(self.tgt_vocab_size + 1, kernel_size=1, activation=gelu, kernel_regularizer=regularizers.l2(self.l2_reg_penalty), name='output_layer') output_softmax_layer = Softmax(name="word_predictions") if self.use_crf: return output_layer(output_dropout_layer(dec_output)) else: return output_softmax_layer(output_layer(output_dropout_layer(dec_output)))
开发者ID:GlassyWing，项目名称:transformer-word-segmenter，代码行数:18，代码来源:__init__.py
示例2: create_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def create_model(input_shape: tuple, nb_classes: int, init_with_imagenet: bool = False, learning_rate: float = 0.01): weights = None if init_with_imagenet: weights = "imagenet" model = VGG16(input_shape=input_shape, classes=nb_classes, weights=weights, include_top=False) # "Shallow" VGG for Cifar10 x = model.get_layer('block3_pool').output x = layers.Flatten(name='Flatten')(x) x = layers.Dense(512, activation='relu')(x) x = layers.Dense(nb_classes)(x) x = layers.Softmax()(x) model = models.Model(model.input, x) loss = losses.categorical_crossentropy optimizer = optimizers.SGD(lr=learning_rate, decay=0.99) model.compile(optimizer, loss, metrics=["accuracy"]) return model
开发者ID:gaborvecsei，项目名称:Federated-Learning-Mini-Framework，代码行数:24，代码来源:models.py
示例3: build_resnet_generator# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def build_resnet_generator(input_shape, n_filters, n_residual_blocks, seq_len, vocabulary_size): inputs = Input(shape=input_shape) # Dense 1: 1 x seq_len x n_filters x = Dense(1 * seq_len * n_filters, input_shape=input_shape)(inputs) x = Reshape((1, seq_len, n_filters))(x) # ResNet blocks x = resnet_block(x, n_residual_blocks, n_filters) # Output layer x = Conv2D(filters=vocabulary_size, kernel_size=1, padding='same')(x) x = Softmax(axis=3)(x) # create model graph model = Model(inputs=inputs, outputs=x, name='Generator') print("/nGenerator ResNet") model.summary() return model
开发者ID:PacktPublishing，项目名称:Hands-On-Generative-Adversarial-Networks-with-Keras，代码行数:23，代码来源:models.py
示例4: __call__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def __call__(self, q, k, v, attn_mask=None, scale=1.0): """ :param q: Queries 张量，形状为[N, T_q, D_q] :param k: Keys 张量，形状为[N, T_k, D_k] :param v: Values 张量，形状为[N, T_v, D_v] :param attn_mask: 注意力掩码，形状为[N, T_q, T_k] :param scale: 缩放因子，浮点标量 :return: 上下文张量和注意力张量 """ attention = Lambda(lambda x: K.batch_dot(x[0], x[1], axes=(2, 2)) * scale)([q, k]) # [N, T_q, T_k] if attn_mask is not None: # 为需要掩码的地方设置一个负无穷，softmax之后就会趋近于0 attention = Lambda(lambda x: (-1e+10) * (1 - x[0]) + x[1])([attn_mask, attention]) attention = Softmax(axis=-1)(attention) attention = Dropout(self.attention_dropout)(attention) # [N, T_q, T_k] context = Lambda(lambda x: K.batch_dot(x[0], x[1], axes=(2, 1)))([attention, v]) # [N, T_q, D_q] return context, attention
开发者ID:GlassyWing，项目名称:transformer-keras，代码行数:21，代码来源:core.py
示例5: test_softmax# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def test_softmax(): for axis in [1, -1]: layer_test(layers.Softmax, kwargs={'axis': axis}, input_shape=(2, 3, 4))
示例6: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def __init__(self, src_vocab_size, src_max_len, tgt_vocab_size, tgt_max_len, optimizer=Adam(lr=1e-3), num_layers=6, model_dim=512, num_heads=8, ffn_dim=2048, dropout=0.2, src_tokenizer=None, tgt_tokenizer=None, weights_path=None): self.optimizer = optimizer self.src_max_len = src_max_len self.tgt_max_len = tgt_max_len self.src_vocab_size = src_vocab_size self.tgt_vocab_size = tgt_vocab_size self.model_dim = model_dim self.num_layers = num_layers self.num_heads = num_heads self.ffn_dim = ffn_dim self.dropout = dropout self.decode_model = None # used in beam_search self.encode_model = None # used in beam_search self.src_tokenizer = src_tokenizer self.tgt_tokenizer = tgt_tokenizer self.encoder = Encoder(src_vocab_size, src_max_len, num_layers, model_dim, num_heads, ffn_dim, dropout) self.decoder = Decoder(tgt_vocab_size, tgt_max_len, num_layers, model_dim, num_heads, ffn_dim, dropout) self.linear = Dense(tgt_vocab_size + 1, use_bias=False) self.softmax = Softmax(axis=2) self.pred_model, self.model = self.__build_model() if weights_path is not None: self.model.load_weights(weights_path)
开发者ID:GlassyWing，项目名称:transformer-keras，代码行数:43，代码来源:core.py
示例7: universal_transformer_gpt_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def universal_transformer_gpt_model( max_seq_length: int, vocabulary_size: int, word_embedding_size: int, transformer_depth: int, num_heads: int, transformer_dropout: float = 0.1, embedding_dropout: float = 0.6, l2_reg_penalty: float = 1e-6, confidence_penalty_weight: float = 0.1): """ A model which is similar to the one described by OpenAI in paper "Improving Language Understanding by Generative Pre-Training", except that it relies L2 regularization of the word embedding matrix (instead of the dropout), and uses Universal Transformer architecture. """ word_ids = Input(shape=(max_seq_length,), dtype='int32', name='word_ids') l2_regularizer = (regularizers.l2(l2_reg_penalty) if l2_reg_penalty else None) embedding_layer = ReusableEmbedding( vocabulary_size, word_embedding_size, input_length=max_seq_length, name='bpe_embeddings', # Regularization is based on paper "A Comparative Study on # Regularization Strategies for Embedding-based Neural Networks" # https://arxiv.org/pdf/1508.03721.pdf embeddings_regularizer=l2_regularizer) output_layer = TiedOutputEmbedding( projection_regularizer=l2_regularizer, projection_dropout=embedding_dropout, name='word_prediction_logits') coordinate_embedding_layer = TransformerCoordinateEmbedding( transformer_depth, name='coordinate_embedding') transformer_act_layer = TransformerACT(name='adaptive_computation_time') transformer_block = TransformerBlock( name='transformer', num_heads=num_heads, residual_dropout=transformer_dropout, attention_dropout=transformer_dropout, use_masking=True, vanilla_wiring=False) output_softmax_layer = Softmax(name='word_predictions') next_step_input, embedding_matrix = embedding_layer(word_ids) act_output = next_step_input for i in range(transformer_depth): next_step_input = coordinate_embedding_layer(next_step_input, step=i) next_step_input = transformer_block(next_step_input) next_step_input, act_output = transformer_act_layer(next_step_input) transformer_act_layer.finalize() next_step_input = act_output word_predictions = output_softmax_layer( output_layer([next_step_input, embedding_matrix])) model = Model(inputs=[word_ids], outputs=[word_predictions]) # Penalty for confidence of the output distribution, as described in # "Regularizing Neural Networks by Penalizing Confident # Output Distributions" (https://arxiv.org/abs/1701.06548) confidence_penalty = K.mean( confidence_penalty_weight * K.sum(word_predictions * K.log(word_predictions), axis=-1)) model.add_loss(confidence_penalty) return model
开发者ID:kpot，项目名称:keras-transformer，代码行数:62，代码来源:models.py
示例8: vanilla_transformer_gpt_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Softmax [as 别名]def vanilla_transformer_gpt_model( max_seq_length: int, vocabulary_size: int, word_embedding_size: int, transformer_depth: int, num_heads: int, transformer_dropout: float = 0.1, embedding_dropout: float = 0.6, l2_reg_penalty: float = 1e-6, confidence_penalty_weight: float = 0.1): """ A model which is almost identical to the one described by OpenAI in paper "Improving Language Understanding by Generative Pre-Training", except that it uses L2 regularization of the word embedding matrix, instead of the dropout. """ word_ids = Input(shape=(max_seq_length,), dtype='int32', name='word_ids') l2_regularizer = (regularizers.l2(l2_reg_penalty) if l2_reg_penalty else None) embedding_layer = ReusableEmbedding( vocabulary_size, word_embedding_size, input_length=max_seq_length, name='bpe_embeddings', # Regularization is based on paper "A Comparative Study on # Regularization Strategies for Embedding-based Neural Networks" # https://arxiv.org/pdf/1508.03721.pdf embeddings_regularizer=l2_regularizer) output_layer = TiedOutputEmbedding( projection_regularizer=l2_regularizer, projection_dropout=embedding_dropout, name='word_prediction_logits') coordinate_embedding_layer = TransformerCoordinateEmbedding( 1, name='coordinate_embedding') output_softmax_layer = Softmax(name='word_predictions') next_step_input, embedding_matrix = embedding_layer(word_ids) next_step_input = coordinate_embedding_layer(next_step_input, step=0) for i in range(transformer_depth): next_step_input = ( TransformerBlock( name='transformer' + str(i), num_heads=num_heads, residual_dropout=transformer_dropout, attention_dropout=transformer_dropout, use_masking=True, vanilla_wiring=True) (next_step_input)) word_predictions = output_softmax_layer( output_layer([next_step_input, embedding_matrix])) model = Model(inputs=[word_ids], outputs=[word_predictions]) # Penalty for confidence of the output distribution, as described in # "Regularizing Neural Networks by Penalizing Confident # Output Distributions" (https://arxiv.org/abs/1701.06548) confidence_penalty = K.mean( confidence_penalty_weight * K.sum(word_predictions * K.log(word_predictions), axis=-1)) model.add_loss(confidence_penalty) return model
开发者ID:kpot，项目名称:keras-transformer，代码行数:59，代码来源:models.py
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