Question

我正在处理由3个子网组成的多CNN，CNN接受6张图像作为输入，每3张图像代表一个人。我将数据标记为：2000对为正数，2000对为负数，但是我想确保批次也保持平衡，例如：64个批次表示32个正数和32个负数。

模型采用以下形状的6个输入：（3024，1，192，192） y_train shape =（1008，1）

但是，当我尝试平衡批次时，fit_gen期望输出以下（x，y）并且我需要以下输出（x [：0] .. X [：5]，y）使其适合模型

发生以下错误

Output of generator should be a tuple `(x, y, sample_weight)` or `(x, y)`. Found: (array([[[[0., 0., 0., ..., 0., 0., 0.],
     [0., 0., 0., ..., 0., 0., 0.],
     [0., 0., 0., ..., 0., 0., 0.],
     ...,
     [0., 0., 0., ..., 0., 0., 0.],
     [0., 0., 0., ..., 0., 0., 0.],
     [0., 0., 0., ..., 0., 0., 0.]]],

我使用以下公式来平衡批次：

img_1 = x_train[:, 0]
img_2 = x_train[:, 1] 
img_3 = x_train[:, 2]
img_4 = x_train[:, 3] 
img_5 = x_train[:, 4]
img_6 = x_train[:, 5]


gen =  balanced_generator(x_train,img_1, img_2, img_3, img_4, img_5, img_6,  y_train, 64)
modelMerged.fit_generator(gen, steps_per_epoch=1, epochs=epochs)

def balanced_generator(x,x1,x2,x3,x4,x5,x6, y, batch_size):      
    batch_x_shape = (batch_size, x.shape[1], x.shape[2], x.shape[3],x.shape[4])
    batch_y_shape = (batch_size, )
    batch_size1 = int(batch_size/2)
    batch_x = np.ndarray(shape=batch_x_shape, dtype=x.dtype)
    batch_y = np.zeros(shape=batch_y_shape, dtype=y.dtype)

    for i in range(batch_size1):
        ind1 = np.random.randint(0,y.shape[0])
        while y[ind1] == 0:
            ind1 = np.random.randint(0,y.shape[0])
        batch_x[i,0,:,:,:] = x1[ind1,:,:,:]
        batch_x[i,1,:,:,:] = x2[ind1,:,:,:]
        batch_x[i,2,:,:,:] = x3[ind1,:,:,:]
        batch_x[i,3,:,:,:] = x4[ind1,:,:,:]
        batch_x[i,4,:,:,:] = x5[ind1,:,:,:]
        batch_x[i,5,:,:,:] = x6[ind1,:,:,:]
        batch_y[i] = y[ind1]

    for i in range(batch_size):
        i = int(batch_size/2)
        ind2 = np.random.randint(0,y.shape[0])
        while y[ind2] == 0:
            ind2 = np.random.randint(0,y.shape[0])
        batch_x[i,0,:,:,:] = x1[ind2,:,:,:]
        batch_x[i,1,:,:,:] = x2[ind2,:,:,:]
        batch_x[i,2,:,:,:] = x3[ind2,:,:,:]
        batch_x[i,3,:,:,:] = x4[ind2,:,:,:]
        batch_x[i,4,:,:,:] = x5[ind2,:,:,:]
        batch_x[i,5,:,:,:] = x6[ind2,:,:,:]
        batch_y[i] = y[ind2]
    print("batch #")   
    yield(batch_x[:, 0],batch_x[:, 1],batch_x[:, 2],batch_x[:, 3],batch_x[:, 4],batch_x[:, 5], batch_y)

Answer 1

fit_generator中的keras仅以(X,Y)格式输入，因此您应将yield部分中的X输出连接到自定义的list中生成器功能：

yield( [batch_x[:, 0],batch_x[:, 1],batch_x[:, 2],batch_x[:, 3],batch_x[:, 4],batch_x[:, 5]], batch_y) # <- Add []

在喀拉拉邦平衡多输入迷你批次样品

1 个答案: