多分类

Keras 深度学习入门

Miguel Esteban

Data Scientist & Founder

掷飞镖

数据集

架构

输出层

多分类模型

# Instantiate a sequential model
# ...

# Add an input and hidden layer
# ...

# Add more hidden layers
# ...

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

交叉熵（多分类）

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

准备数据集

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 vector
y = to_categorical(df.response)

独热编码（One-hot）

¡Vamos a practicar!

Keras 深度学习入门