utils.py 文件源码

def conv2d( inputs , outputs_dim , kernel_size ,   stride ,   padding = "SAME" , he_init = False , activation_fn = None , regularization_scale = 0.0  ): 
    C = inputs.get_shape()[-1].value
    fan_in = C * kernel_size**2
    fan_out = C * kernel_size**2 / stride**2
    avg_fan = (fan_in + fan_out) / 2
    if he_init:
        var = 2.0/avg_fan
    else :
        var = 1.0/avg_fan
    # var = (b - a)**2 / 12 , b==-a ,  (zero mean)
    upper_bound = np.sqrt( 12.0*var ) * 0.5 
    weights_initializer = tf.random_uniform_initializer( -upper_bound , upper_bound , seed = None , dtype = tf.float32 )
    weights_regularizer = layers.l2_regularizer( scale = regularization_scale )
    return layers.conv2d( inputs = inputs , num_outputs = outputs_dim , kernel_size = kernel_size , stride =  stride, padding = "SAME"  , activation_fn = activation_fn , weights_initializer = weights_initializer   , weights_regularizer = weights_regularizer )