Python keras.layers.Softmax() Examples

The following are 14 code examples of keras.layers.Softmax(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.layers , or try the search function

Example #1

Source File: __init__.py From transformer-word-segmenter with Apache License 2.0

6 votes

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)))

Example #2

Source File: models.py From Federated-Learning-Mini-Framework with MIT License

6 votes

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

Example #3

Source File: models.py From Hands-On-Generative-Adversarial-Networks-with-Keras with MIT License

6 votes

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

Example #4

Source File: core.py From transformer-keras with Apache License 2.0

6 votes

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

Example #5