多類別分類

Keras 深度學習入門

Miguel Esteban

Data Scientist & Founder

丟飛鏢

資料集

架構

輸出層

多類別模型

# Instantiate a sequential model
# ...

# Add an input and hidden layer
# ...

# Add more hidden layers
# ...

# Add your output layer
model.add(Dense(4, activation='softmax')

類別交叉熵

model.compile(optimizer='adam', loss='categorical_crossentropy')

準備資料集

import pandas as pd
from tensorflow.keras.utils import to_categorical

# Load dataset
df = pd.read_csv('data.csv')

# Turn response variable into labeled codes
df.response = pd.Categorical(df.response)
df.response = df.response.cat.codes

# Turn response variable into one-hot response vectory = to_categorical(df.response)

One-hot 編碼

一起來練習吧！

Keras 深度學習入門