INNER CODE UNIT · Python

conv5_flat

aiff22/DPED · models.py:70

        conv5_flat = tf.reshape(conv5, [-1, flat_size])

        W_fc = tf.Variable(tf.compat.v1.truncated_normal([flat_size, 1024], stddev=0.01))
        bias_fc = tf.Variable(tf.constant(0.01, shape=[1024]))

        fc = leaky_relu(tf.matmul(conv5_flat, W_fc) + bias_fc)

        W_out = tf.Variable(tf.compat.v1.truncated_normal([1024, 2], stddev=0.01))
        bias_out = tf.Variable(tf.constant(0.01, shape=[2]))

        adv_out = tf.nn.softmax(tf.matmul(fc, W_out) + bias_out)
    
    return adv_out


def weight_variable(shape, name):

    initial = tf.compat.v1.truncated_normal(shape, stddev=0.01)

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