TypeError：float() 参数必须是字符串或数字，而不是 'builtin_function_or_method'

Question

我正在运行一个检查图像但不分类的 CNN。实际上，输出层是一个密集层，其参数是 1d 标签中图像的大小。

如下代码所示，我使用的是 model.fit_generator() 而不是 model.fit，当开始训练模型时出现以下错误：

TypeError: float() 参数必须是字符串或数字，而不是 'builtin_function_or_method'

我真的不明白为什么会这样。 这里附上模型的摘要：

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层（类型）输出形状参数#

conv2d_4 (Conv2D) (无, 26, 877, 32) 544

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activation_5 (激活) (None, 26, 877, 32) 0

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max_pooling2d_4 (MaxPooling2 (None, 13, 438, 32) 0

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conv2d_5 (Conv2D) (无, 12, 437, 16) 2064

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activation_6 (激活) (None, 12, 437, 16) 0

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max_pooling2d_5 (MaxPooling2 (None, 6, 218, 16) 0

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conv2d_6 (Conv2D) (无, 5, 217, 8) 520

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activation_7 (激活) (None, 5, 217, 8) 0

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max_pooling2d_6 (MaxPooling2 (None, 2, 108, 8) 0

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activation_8 (激活) (None, 2, 108, 8) 0

---

flatten_2（展平）（无，1728）0

---

dropout_2（辍学）（无，1728）0

---

dense_2（密集）（无，19316）33397364

================================================ ===================

总参数：33,400,492 可训练参数：33,400,492 不可训练参数：0

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有什么建议吗？ 提前非常感谢！

我已经查找了许多在线论坛/网站，但似乎没有找到适合我的情况。

def generator(data_arr, batch_size = 10):

num = len(data_arr) 

if num % batch_size != 0 : 
    num = int(num/batch_size)

# Loop forever so the generator never terminates
while True: 

    for offset in range(0, num, batch_size):

        batch_samples = (data_arr[offset:offset+batch_size])

        samples = []
        labels = []

        for batch_sample in batch_samples:

            samples.append(batch_sample[0])
            labels.append((np.array(batch_sample[1].flatten)).transpose())

        X_ = np.array(samples)
        Y_ = np.array(labels)

        X_ = X_[:, :, :, newaxis]

        print(X_.shape)
        print(Y_.shape)

        yield (X_, Y_)

# compile and train the model using the generator function
train_generator = generator(training_data, batch_size = 10)
validation_generator = generator(val_data, batch_size = 10)

run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)

model = Sequential()

model.add(Conv2D(32, (4, 4), strides=(2, 2), input_shape = (55, 1756, 
1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(16, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(8, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Activation('softmax'))
model.add(Flatten())  # this converts our 3D feature maps to 1D feature 
vectors
model.add(Dropout(0.3))
model.add(Dense(19316))

model.compile(loss = 'sparse_categorical_crossentropy',
              optimizer = 'adam',
              metrics = ['accuracy'],
              options = run_opts)

model.summary()

batch_size = 20
nb_epoch = 6

model.fit_generator(train_generator, 
                    steps_per_epoch = len(training_data) ,
                    epochs = nb_epoch,
                    validation_data = validation_generator,
                    validation_steps = len(val_data))

Answer 1

大概就是这一行

labels.append((np.array(batch_sample[1].flatten)).transpose())

应该是

labels.append((np.array(batch_sample[1].flatten())).transpose())