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18 def dense(inputs, units): 19 return tf.layers.dense(inputs, units, 20 kernel_initializer=tf.random_normal_initializer(stddev=0.02), 21 bias_initializer=tf.constant_initializer(0.0))
72 def add_dense(self): 73 # self.model.add(MaxoutDense(output_size 74 # , output_size 75 # , W_regularizer=l2(.01) 76 # , init=self.conf['--initialization'])) 77 self.model.add(Dense(int(self.conf['--input_dim']) 78 , 1 79 , init=self.conf['--initialization'] 80 , activation=self.conf['--activation'])) 81 # self.model.add(Activation('softmax')) 82 # model.add(Activation(conf['--activation'])) 83 84 return self.model
113 def model(inputs): 114 conv_1 = Conv2D(64, (10, 10), activation='relu', kernel_initializer=W_init, kernel_regularizer=l2(2e-4))(inputs) 115 # conv_1 = Conv2D(64, (10, 10), activation='relu')(inputs) 116 pool_1 = MaxPooling2D()(conv_1) 117 118 conv_2 = Conv2D(128,(7,7),activation='relu', kernel_regularizer=l2(2e-4),kernel_initializer=W_init,bias_initializer=b_init)(pool_1) 119 # conv_2 = Conv2D(128,(7,7),activation='relu')(pool_1) 120 pool_2 = MaxPooling2D()(conv_2) 121 122 conv_3 = Conv2D(128,(4,4),activation='relu',kernel_initializer=W_init,kernel_regularizer=l2(2e-4),bias_initializer=b_init)(pool_2) 123 pool_3 = MaxPooling2D()(conv_3) 124 125 conv_4 = Conv2D(256,(4,4),activation='relu',kernel_initializer=W_init,kernel_regularizer=l2(2e-4),bias_initializer=b_init)(pool_3) 126 127 128 f = Flatten()(conv_4) 129 130 dense_1 = Dense(4096, activation="sigmoid",kernel_regularizer=l2(1e-3),kernel_initializer=W_init,bias_initializer=b_init) 131 return dense_1
122 def Dense_net(self, input_x): 123 x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], layer_name='conv0') 124 x = Max_Pooling(x, pool_size=3, stride=2) 125 126 127 """ 128 for i in range(self.nb_blocks) : 129 # 6 -> 12 -> 32 130 131 x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i)) 132 x = self.transition_layer(x, scope='trans_'+str(i)) 133 """ 134 135 136 137 x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1') 138 x = self.transition_layer(x, scope='trans_1') 139 140 x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2') 141 x = self.transition_layer(x, scope='trans_2') 142 143 x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_3') 144 x = self.transition_layer(x, scope='trans_3') 145 146 147 x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final') # in paper, nb_layers = 32 148 149 x = Relu(x) 150 x = Global_Average_Pooling(x) 151 x = Linear(x) 152 153 x = tf.reshape(x, [-1, 10]) 154 return x
113 def call(self, x): 114 if self.equalized_lr: 115 kernel = self.kernel*self.wscale 116 else: 117 kernel = self.kernel 118 x = tf.matmul(x, kernel) 119 if self.use_bias: 120 x = tf.nn.bias_add(x, self.bias) 121 return x