python - Tensorflow 模型训练：缺少 1 个必需的位置参数：'self'

Question

我正在尝试按照训练你的第一个神经网络的示例练习神经网络分类器：基本分类，这是我的代码，直到模型训练点：

import tensorflow as tf
from tensorflow import keras

import numpy as np
from matplotlib.pyplot import show
import matplotlib.pyplot as plt

from matplotlib.pyplot import figure
from matplotlib.pyplot import imshow
from matplotlib.pyplot import colorbar
from matplotlib.pyplot import axis
from matplotlib.pyplot import plot
from matplotlib.pyplot import show

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


#figure(); imshow(train_images[1]); colorbar(); axis('auto') 

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

N1, N2, N3 = test_images.shape

train_images = train_images / 255.0
test_images  = test_images / 255.0

model = keras.Sequential
([
    keras.layers.Flatten(input_shape=(N2, N3)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])


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


model.fit(train_images, train_labels, epochs=5)

它返回错误

TypeError: _method_wrapper() missing 1 required positional argument: 'self'

这发生在

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

我google了一下，好像

m = model()
m.compile()

可以避免“自我”错误。然而，它得到了新的错误，训练仍然没有发生。

我只是想知道我应该如何修改代码，以便我可以让模型像这样训练：

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [==============================] - 1s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
8192/5148 [===============================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4423680/4422102 [==============================] - 0s 0us/step

标签： pythonclasstensorflowmodel

Answer 1

对您的代码进行了一些细微的修改。希望你能跟进。我没有Sequential()在model.

import tensorflow as tf
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from matplotlib.pyplot import show
import matplotlib.pyplot as plt

from matplotlib.pyplot import figure
from matplotlib.pyplot import imshow
from matplotlib.pyplot import colorbar
from matplotlib.pyplot import axis
from matplotlib.pyplot import plot
from matplotlib.pyplot import show

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


#figure(); imshow(train_images[1]); colorbar(); axis('auto') 

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

N1, N2, N3 = test_images.shape

train_images = train_images / 255.0
test_images  = test_images / 255.0
# model = Sequential
# ([
#     keras.layers.Flatten(input_shape=(N2, N3)),
#     keras.layers.Dense(128, activation=tf.nn.relu),
#     keras.layers.Dense(10, activation=tf.nn.softmax)
# ])

model= Sequential()
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dense(10, activation=tf.nn.softmax))

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


model.fit(train_images.reshape(len(train_images),784), train_labels, epochs=5)

使用此代码，它运行如下。

32/60000 [.......................] - ETA：3:02 - 损失：2.6468 - acc：0

1344/60000 [.......................] - ETA：6s - 损失：1.3037 - acc：0.5

2816/60000 [>.......................] - ETA：4s - 损失：1.0207 - acc：0.6

4256/60000 [=>.......................] - ETA：3s - 损失：0.9073 - acc：0.6

5632/60000 [=>.......................] - ETA：2s - 损失：0.8394 - acc：0.7

7104/60000 [==>.......................] - ETA：2s - 损失：0.7912 - acc：0.7