5 examples of 'tf.keras.layers.dense' in Python

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def dense(inputs, units):
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    return tf.layers.dense(inputs, units, 
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        kernel_initializer=tf.random_normal_initializer(stddev=0.02),
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        bias_initializer=tf.constant_initializer(0.0))

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def add_dense(self):
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    # self.model.add(MaxoutDense(output_size
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    #                            , output_size
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    #                            , W_regularizer=l2(.01)
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    #                            , init=self.conf['--initialization']))
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    self.model.add(Dense(int(self.conf['--input_dim'])
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                         , 1
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                         , init=self.conf['--initialization']
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                         , activation=self.conf['--activation']))
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    # self.model.add(Activation('softmax'))
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    # model.add(Activation(conf['--activation']))
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    return self.model

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def model(inputs):
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    conv_1 = Conv2D(64, (10, 10), activation='relu', kernel_initializer=W_init, kernel_regularizer=l2(2e-4))(inputs)
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    # conv_1 = Conv2D(64, (10, 10), activation='relu')(inputs)
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    pool_1 = MaxPooling2D()(conv_1)
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    conv_2 = Conv2D(128,(7,7),activation='relu', kernel_regularizer=l2(2e-4),kernel_initializer=W_init,bias_initializer=b_init)(pool_1)
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    # conv_2 = Conv2D(128,(7,7),activation='relu')(pool_1)
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    pool_2 = MaxPooling2D()(conv_2)
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    conv_3 = Conv2D(128,(4,4),activation='relu',kernel_initializer=W_init,kernel_regularizer=l2(2e-4),bias_initializer=b_init)(pool_2)
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    pool_3 = MaxPooling2D()(conv_3)
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    conv_4 = Conv2D(256,(4,4),activation='relu',kernel_initializer=W_init,kernel_regularizer=l2(2e-4),bias_initializer=b_init)(pool_3)
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    f = Flatten()(conv_4)
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    dense_1 = Dense(4096, activation="sigmoid",kernel_regularizer=l2(1e-3),kernel_initializer=W_init,bias_initializer=b_init)
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    return dense_1

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def Dense_net(self, input_x):
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    x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], layer_name='conv0')
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    x = Max_Pooling(x, pool_size=3, stride=2)
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    """
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    for i in range(self.nb_blocks) :
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        # 6 -> 12 -> 32
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        x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))
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        x = self.transition_layer(x, scope='trans_'+str(i))
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    """
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    x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')
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    x = self.transition_layer(x, scope='trans_1')
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    x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')
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    x = self.transition_layer(x, scope='trans_2')
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    x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_3')
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    x = self.transition_layer(x, scope='trans_3')
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    x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')  # in paper, nb_layers = 32
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    x = Relu(x)
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    x = Global_Average_Pooling(x)
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    x = Linear(x)
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    x = tf.reshape(x, [-1, 10])
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    return x

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def call(self, x):
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    if self.equalized_lr:
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        kernel = self.kernel*self.wscale
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    else:
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        kernel = self.kernel
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    x = tf.matmul(x, kernel)
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    if self.use_bias:
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        x = tf.nn.bias_add(x, self.bias)
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    return x