用於多類分類的 ANN 模型

Question

我不知道問題是什么以及為什么我會收到此錯誤：

ValueError: in user code:
ValueError: Shapes (None, 1) and (None, 6) are incompatible

任何人都可以幫我處理這段代碼嗎？

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation,Dropout
from sklearn.preprocessing import MinMaxScaler
%matplotlib inline

df = pd.read_csv('test.csv')
dft = pd.read_csv('train.csv')

X_train = df.drop('label',axis=1).values
y_train = df['label'].values

X_test = dft.drop('label',axis=1).values
y_test = dft['label'].values

scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

model = Sequential()
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=15, activation='relu'))
model.add(Dense(6, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=X_train, y=y_train, epochs=15, batch_size=10, validation_data=(X_test, y_test))

Answer 1

問題是目標數組（ y_train和y_test ）的第二維長度等於 1，而模型期望 6，假設輸出層的神經元數量設置為 6。要解決這個問題您需要對目標進行一次性編碼的問題（您可以使用 scikit-learn OneHotEncoder ）。 如果您的目標確實有 6 個類，那么您的模型將起作用。

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
tf.random.set_seed(0)

# generate the data
X, y = make_classification(n_classes=6, n_samples=1000, n_features=10, n_informative=10, n_redundant=0, random_state=42)
print(y.shape)
# (1000, )

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# one-hot encode the target
enc = OneHotEncoder(sparse=False, handle_unknown='ignore')
enc.fit(y_train.reshape(-1, 1))
y_train = enc.transform(y_train.reshape(-1, 1))
y_test = enc.transform(y_test.reshape(-1, 1))
print(y_train.shape, y_test.shape)
# (750, 6) (250, 6)

# scale the features
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# define the model
model = Sequential()
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=15, activation='relu'))
model.add(Dense(6, activation='softmax'))

# fit the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=X_train, y=y_train, epochs=3, batch_size=10, validation_data=(X_test, y_test))
# Epoch 1/3
# 75/75 [==============================] - 1s 2ms/step - loss: 1.7872 - accuracy: 0.2427 - val_loss: 1.7719 - val_accuracy: 0.2600
# Epoch 2/3
# 75/75 [==============================] - 0s 781us/step - loss: 1.7660 - accuracy: 0.2547 - val_loss: 1.7549 - val_accuracy: 0.2720
# Epoch 3/3
# 75/75 [==============================] - 0s 768us/step - loss: 1.7528 - accuracy: 0.2587 - val_loss: 1.7408 - val_accuracy: 0.3280