2d_convolutional_net.py 文件源码

def model(X, w, w2, w3, w35, w4, p_drop_conv, p_drop_hidden):
    l1a = rectify(conv2d(X, w, border_mode='full'))
    #print "l1a",l1a.type
    #print "l1a",l1a.shape.eval()
    l1 = max_pool_2d(l1a, (2, 2))
    #print "l1",l1.get_value().shape
    #l1 = dropout(l1, p_drop_conv)

    l2a = rectify(conv2d(l1, w2))
    #print "l2a",l2a.get_value().shape
    l2 = max_pool_2d(l2a, (2, 2))
    #print "l2",l2.get_value().shape
    #l2 = dropout(l2, p_drop_conv)

    l3 = rectify(conv2d(l2, w3))
    #print "l3",l3.get_value().shape
    #l3 = max_pool_2d(l3a, (1, 1))
    #l3 = dropout(l3, p_drop_conv)

    l35a = rectify(conv2d(l3, w35))
    #print "l35a",l35a.get_value().shape
    l35b = max_pool_2d(l35a, (2, 2))
    #print "l35b",l35b.get_value().shape
    l35 = T.flatten(l35b, outdim=2)
    #print "l35",l35.get_value().shape
    #l35 = dropout(l35, p_drop_conv)

    l4 = rectify(T.dot(l35, w4))
    #print "l4",l4.get_value().shape
    #l4 = dropout(l4, p_drop_hidden)

    pyx = softmax(T.dot(l4, w_o))
    return l1, l2, l3, l35, l4, pyx