python - 对于 MNIST 图像，前馈 ANN 的测试精度一直停留在 42%

标签 python tensorflow mnist feed-forward

我在 mnist 数据集上使用了原始神经网络，但是我的模式在验证数据的准确度上停留在 42%。
数据为 csv，格式为:60000 行(用于训练数据)和 785 列，第一个是标签。
以下是分割和转换CSV数据的代码，表示图像(28x28):

import pandas as pd 
import numpy as np
import tensorflow as tf

df = pd.read_csv('mnist_train.csv')
dff = pd.read_csv('mnist_test.csv')

#train set
label = np.array(df.iloc[:,0])
data = np.array(df.iloc[:,1:])
sep = []
for i in range(60000):
    temp = []
    for j in range(28):
        temp.append(data[i,j*28:(j+1)*28])
    sep.append(temp)
    
sep = np.array(sep)
for i in range(60000):
    for j in range(28):
        for k in range(28):
            sep[i,j,k] = sep[i,j,k]/255
labels_array = []
for i in label:
    if i==0:
        labels_array.append([1,0,0,0,0,0,0,0,0,0])
    if i==1:
        labels_array.append([0,1,0,0,0,0,0,0,0,0])
    if i==2:
        labels_array.append([0,0,1,0,0,0,0,0,0,0])
    if i==3:
        labels_array.append([0,0,0,1,0,0,0,0,0,0])
    if i==4:
        labels_array.append([0,0,0,0,1,0,0,0,0,0])
    if i==5:
        labels_array.append([0,0,0,0,0,1,0,0,0,0])
    if i==6:
        labels_array.append([0,0,0,0,0,0,1,0,0,0])
    if i==7:
        labels_array.append([0,0,0,0,0,0,0,1,0,0])
    if i==8:
        labels_array.append([0,0,0,0,0,0,0,0,1,0])
    if i==9:
        labels_array.append([0,0,0,0,0,0,0,0,0,1])

labels_array = np.array(labels_array)

#train
label_t = np.array(dff.iloc[:,0])
data_t = np.array(dff.iloc[:,1:])
sep_t = []
for i in range(10000):
    temp = []
    for j in range(28):
        temp.append(data_t[i,j*28:(j+1)*28])
    sep_t.append(temp)
    
sep_t = np.array(sep_t)

for i in range(10000):
    for j in range(28):
        for k in range(28):
            sep_t[i,j,k] = sep_t[i,j,k]/255

labels_array_t = []
for i in label_t:
    if i==0:
        labels_array_t.append([1,0,0,0,0,0,0,0,0,0])
    if i==1:
        labels_array_t.append([0,1,0,0,0,0,0,0,0,0])
    if i==2:
        labels_array_t.append([0,0,1,0,0,0,0,0,0,0])
    if i==3:
        labels_array_t.append([0,0,0,1,0,0,0,0,0,0])
    if i==4:
        labels_array_t.append([0,0,0,0,1,0,0,0,0,0])
    if i==5:
        labels_array_t.append([0,0,0,0,0,1,0,0,0,0])
    if i==6:
        labels_array_t.append([0,0,0,0,0,0,1,0,0,0])
    if i==7:
        labels_array_t.append([0,0,0,0,0,0,0,1,0,0])
    if i==8:
        labels_array_t.append([0,0,0,0,0,0,0,0,1,0])
    if i==9:
        labels_array_t.append([0,0,0,0,0,0,0,0,0,1])

labels_array_t = np.array(labels_array_t)
以下是学习网络:
Dense = tf.keras.layers.Dense
fc_model = tf.keras.Sequential(
    [
      tf.keras.Input(shape=(28,28)),
      tf.keras.layers.Flatten(),
      Dense(128, activation='relu'),
      Dense(32, activation='relu'),
      Dense(10, activation='softmax')])
fc_model.compile(optimizer="Adam", loss="categorical_crossentropy", metrics=["accuracy"])
history = fc_model.fit(sep, labels_array, batch_size=128, validation_data=(sep_t, labels_array_t) ,epochs=35)
以下是我得到的结果:
Train on 60000 samples, validate on 10000 samples
Epoch 1/35
60000/60000 [==============================] - 2s 31us/sample - loss: 1.8819 - accuracy: 0.3539 - val_loss: 1.6867 - val_accuracy: 0.4068
Epoch 2/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.6392 - accuracy: 0.4126 - val_loss: 1.6407 - val_accuracy: 0.4098
Epoch 3/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.5969 - accuracy: 0.4224 - val_loss: 1.6202 - val_accuracy: 0.4196
Epoch 4/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.5735 - accuracy: 0.4291 - val_loss: 1.6158 - val_accuracy: 0.4220
Epoch 5/35
60000/60000 [==============================] - 1s 25us/sample - loss: 1.5561 - accuracy: 0.4324 - val_loss: 1.6089 - val_accuracy: 0.4229
Epoch 6/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.5423 - accuracy: 0.4377 - val_loss: 1.6074 - val_accuracy: 0.4181
Epoch 7/35
60000/60000 [==============================] - 2s 25us/sample - loss: 1.5309 - accuracy: 0.4416 - val_loss: 1.6053 - val_accuracy: 0.4226
Epoch 8/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.5207 - accuracy: 0.4435 - val_loss: 1.6019 - val_accuracy: 0.4252
Epoch 9/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.5111 - accuracy: 0.4480 - val_loss: 1.6015 - val_accuracy: 0.4233
Epoch 10/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.5020 - accuracy: 0.4517 - val_loss: 1.6038 - val_accuracy: 0.4186
Epoch 11/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4954 - accuracy: 0.4530 - val_loss: 1.6096 - val_accuracy: 0.4209
Epoch 12/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4885 - accuracy: 0.4554 - val_loss: 1.6003 - val_accuracy: 0.4278
Epoch 13/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4813 - accuracy: 0.4573 - val_loss: 1.6072 - val_accuracy: 0.4221
Epoch 14/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4749 - accuracy: 0.4598 - val_loss: 1.6105 - val_accuracy: 0.4242
Epoch 15/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4693 - accuracy: 0.4616 - val_loss: 1.6160 - val_accuracy: 0.4213
Epoch 16/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4632 - accuracy: 0.4626 - val_loss: 1.6149 - val_accuracy: 0.4266
Epoch 17/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4580 - accuracy: 0.4642 - val_loss: 1.6145 - val_accuracy: 0.4267
Epoch 18/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4532 - accuracy: 0.4656 - val_loss: 1.6169 - val_accuracy: 0.4330
Epoch 19/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4479 - accuracy: 0.4683 - val_loss: 1.6198 - val_accuracy: 0.4236
Epoch 20/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4436 - accuracy: 0.4693 - val_loss: 1.6246 - val_accuracy: 0.4264
Epoch 21/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4389 - accuracy: 0.4713 - val_loss: 1.6300 - val_accuracy: 0.4254
Epoch 22/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4350 - accuracy: 0.4730 - val_loss: 1.6296 - val_accuracy: 0.4258
Epoch 23/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4328 - accuracy: 0.4727 - val_loss: 1.6279 - val_accuracy: 0.4257
Epoch 24/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4282 - accuracy: 0.4742 - val_loss: 1.6327 - val_accuracy: 0.4209
Epoch 25/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4242 - accuracy: 0.4745 - val_loss: 1.6387 - val_accuracy: 0.4256
Epoch 26/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4210 - accuracy: 0.4765 - val_loss: 1.6418 - val_accuracy: 0.4240
Epoch 27/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4189 - accuracy: 0.4773 - val_loss: 1.6438 - val_accuracy: 0.4237
Epoch 28/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4151 - accuracy: 0.4781 - val_loss: 1.6526 - val_accuracy: 0.4184
Epoch 29/35
60000/60000 [==============================] - 1s 25us/sample - loss: 1.4129 - accuracy: 0.4788 - val_loss: 1.6572 - val_accuracy: 0.4190
Epoch 30/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4097 - accuracy: 0.4801 - val_loss: 1.6535 - val_accuracy: 0.4225
Epoch 31/35
60000/60000 [==============================] - 1s 24us/sample - loss: 1.4070 - accuracy: 0.4795 - val_loss: 1.6689 - val_accuracy: 0.4188
Epoch 32/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4053 - accuracy: 0.4809 - val_loss: 1.6663 - val_accuracy: 0.4194
Epoch 33/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4029 - accuracy: 0.4831 - val_loss: 1.6618 - val_accuracy: 0.4220
Epoch 34/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.4000 - accuracy: 0.4832 - val_loss: 1.6603 - val_accuracy: 0.4270
Epoch 35/35
60000/60000 [==============================] - 1s 23us/sample - loss: 1.3979 - accuracy: 0.4845 - val_loss: 1.6741 - val_accuracy: 0.4195
这只是因为优化器吗？我试过新元但有用!

最佳答案

TLDR； 将损失更改为 categorical_crossentropy
优化器在这里不是问题。
我可以看到的直接问题是，对于多类分类问题，您使用的损失为 mse .请改成categorical_crossentropy .那应该会让你得到更好的数字。另外，不要忘记删除 mse也来自指标。

fc_model.compile(optimizer="Adam", loss="categorical_crossentropy", metrics=["accuracy"])
为了将来引用，您可以使用下表获取最佳实践。如果您花时间研究为什么这些激活函数和损失函数中的每一个都用于数学上的特定问题，那就更好了。
enter image description here

注:另一边注意，即使这不会影响任何性能，您也不需要将标签转换为单热向量。
# YOU CAN SKIP THIS COMPLETELY
for i in label_t:
    if i==0:
        labels_array_t.append([1,0,0,0,0,0,0,0,0,0])
    if i==1:
        labels_array_t.append([0,1,0,0,0,0,0,0,0,0])
    if i==2:
        labels_array_t.append([0,0,1,0,0,0,0,0,0,0])
    if i==3:
        labels_array_t.append([0,0,0,1,0,0,0,0,0,0])
    if i==4:
        labels_array_t.append([0,0,0,0,1,0,0,0,0,0])
    .....
相反，您可以使用原始 label或 label_t直接作为您的 y_train而不是使用损失 categorical_crossentropy您可以将其更改为 sparse_categorical_crossentropy
编辑:
根据您的评论以及我在另一个 mnist 数据集上进行的测试，请尝试以下操作 -
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128,activation='relu'),
  tf.keras.layers.Dense(10)
])
model.compile(
    optimizer='adam',
    loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'],
)

model.fit(
    ds_train,
    epochs=6,
    validation_data=ds_test,
)

关于python - 对于 MNIST 图像，前馈 ANN 的测试精度一直停留在 42%，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/66181402/

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