Python 将元组参数传递给Tensorflow中的Conv2D层时如何修复它

我试图复制the code in this web，但将模型实现为一个类，并进行了一些修改.

%matplotlib inline
from itertools import islice
import itertools
from itertools import count 

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tqdm import trange
from tensorflow.keras import Model
from keras.utils import to_categorical


(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

x_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0

x_train = x_train.reshape((60000, 28, 28, 1))
x_train= x_train.astype('float32') / 255 # rescale pixel values from range [0, 255] to [0, 1]

x_test = x_test.reshape((10000, 28, 28, 1))
x_test = x_test.astype('float32') / 255

train_labels = to_categorical(y_train)
test_labels = to_categorical(y_test)

print("Number of original training examples:", len(x_train))
print("Number of original test examples:", len(x_test))

data_mean = 0.1307
data_std = 0.3081

# Normalize the data
x_train = (x_train/255.0 - data_mean) / data_std
x_test = (x_test/255.0 - data_mean) / data_std
num_classes = 10
input_shape = (28, 28, 1)

# Decay the learning rate at a base rate of gamma roughly every epoch, which
# is len(x_train) steps
scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
    1,
    decay_steps=len(x_train),
    decay_rate=0.7)

# Define the optimizer to user for gradient descent
optimizer = tf.keras.optimizers.Adadelta(scheduler)
loss_object = tf.keras.losses.CategoricalCrossentropy()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalCrossentropy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalCrossentropy(name='test_accuracy')

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)


class MyModel(tf.keras.models.Sequential):
    def __init__(self):
        super(MyModel, self).__init__()
        self.c1 = tf.keras.layers.Conv2D(32, (3,3), strides=(1,1),
                                      padding='valid', 
                                      activation='relu',
                                      input_shape=input_shape),
        self.max2d = tf.keras.layers.MaxPool2D(),
        self.c2 = tf.keras.layers.Conv2D(64, (3,3), strides=(1,1),
                                        padding='valid',
                                        activation='relu'),
        self.dropout1 = tf.keras.layers.Dropout(0.25),
        self.flatten = tf.keras.layers.Flatten(),
        self.dense1 = tf.keras.layers.Dense(64, activation='relu'),
        self.dropout2 = tf.keras.layers.Dropout(0.5),
        self.dense2 = tf.keras.layers.Dense(num_classes, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.c2 (x)
        x = self.max2d(x)
        x = self.dropout1(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dropout2(x)
        x = self.dense2(x)
        return x

model = MyModel()

train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

EPOCHS = 5

for epoch in range(EPOCHS):
    for images, labels in train_ds:        
        train_step(images, labels)  # <----- Boom!!

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

我在Visual Studio代码中遇到的完整错误是:

TypeError                                 Traceback (most recent call last)
d:\Machine_Learning\tensorflow_test.ipynb Cell 5' in <module>
      6 for epoch in range(EPOCHS):
      7     for images, labels in train_ds:        
----> 8         train_step(images, labels)
     10     for test_images, test_labels in test_ds:
     11         test_step(test_images, test_labels)

File c:\Python38\lib\site-packages\tensorflow\python\util\traceback_utils.py:153, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    151 except Exception as e:
    152   filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153   raise e.with_traceback(filtered_tb) from None
    154 finally:
    155   del filtered_tb

File ~\AppData\Local\Temp\__autograph_generated_file14pr1250.py:9, in outer_factory.<locals>.inner_factory.<locals>.tf__train_step(images, labels)
      7 with ag__.FunctionScope('train_step', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:
      8     with ag__.ld(tf).GradientTape() as tape:
----> 9         predictions = ag__.converted_call(ag__.ld(model), (ag__.ld(images),), None, fscope)
     10         loss = ag__.converted_call(ag__.ld(loss_object), (ag__.ld(labels), ag__.ld(predictions)), None, fscope)
     11     gradients = ag__.converted_call(ag__.ld(tape).gradient, (ag__.ld(loss), ag__.ld(model).trainable_variables), None, fscope)

File c:\Python38\lib\site-packages\keras\utils\traceback_utils.py:69, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     66   filtered_tb = _process_traceback_frames(e.__traceback__)
     67   # To get the full stack trace, call:
     68   # `tf.debugging.disable_traceback_filtering()`
---> 69   raise e.with_traceback(filtered_tb) from None
     70 finally:
     71   del filtered_tb

File ~\AppData\Local\Temp\__autograph_generated_filecwsasoc4.py:10, in outer_factory.<locals>.inner_factory.<locals>.tf__call(self, x)
      8 do_return = False
      9 retval_ = ag__.UndefinedReturnValue()
---> 10 x = ag__.converted_call(ag__.ld(self).c1, (ag__.ld(x),), None, fscope)
     11 x = ag__.converted_call(ag__.ld(self).c2, (ag__.ld(x),), None, fscope)
     12 x = ag__.converted_call(ag__.ld(self).max2d, (ag__.ld(x),), None, fscope)

TypeError: in user code:

    File "C:\Users\user\AppData\Local\Temp\ipykernel_17788\3342806354.py", line 21, in train_step  *
        predictions = model(images)
    File "c:\Python38\lib\site-packages\keras\utils\traceback_utils.py", line 69, in error_handler  **
        raise e.with_traceback(filtered_tb) from None
    File "C:\Users\user\AppData\Local\Temp\__autograph_generated_filecwsasoc4.py", line 10, in tf__call
        x = ag__.converted_call(ag__.ld(self).c1, (ag__.ld(x),), None, fscope)

    TypeError: Exception encountered when calling layer "my_model_7" (type MyModel).
    
    in user code:
    
        File "C:\Users\user\AppData\Local\Temp\ipykernel_17788\2012441124.py", line 56, in call  *
            x = self.c1(x)
    
        TypeError: '_TupleWrapper' object is not callable
    
    
    Call arguments received by layer "my_model_7" (type MyModel):
      • x=tf.Tensor(shape=(32, 28, 28, 1), dtype=float32)

所以，我知道我正在向c1层传递一个元组，而其他数据类型是需要的.如何修复此错误？

Python版本:3.8.3 x64

Tensorflow版本:2.10.0-dev20220517

硬版本:2.10.0

操作系统:windows 10

Edit:

愚蠢的尾随逗号让我浪费了两天的调试时间.声明模型和修复错误的正确方法是:

class MyModel(tf.keras.models.Sequential):
    def __init__(self):
        super(MyModel, self).__init__()
        self.c1 = tf.keras.layers.Conv2D(32, (3,3), strides=(1,1),
                                      padding='valid', 
                                      activation='relu',
                                      input_shape=input_shape)
        self.max2d = tf.keras.layers.MaxPool2D()
        self.c2 = tf.keras.layers.Conv2D(64, (3,3), strides=(1,1),
                                        padding='valid',
                                        activation='relu')
        self.dropout1 = tf.keras.layers.Dropout(0.25)
        self.flatten = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dropout2 = tf.keras.layers.Dropout(0.5)
        self.dense2 = tf.keras.layers.Dense(num_classes, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.c2 (x)
        x = self.max2d(x)
        x = self.dropout1(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dropout2(x)
        x = self.dense2(x)
        return x

万分感谢，彼得罗·丹托诺！