利用Keras进行多类分类

针对网上查到的代码运行报错，修改后运行成功。

# -*- coding:UTF-8 -*-
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.preprocessing import LabelEncoder
 

# load dataset
dataframe = pd.read_csv("F:\\testdata\\iris\\iris.csv", header=None )
dataset = dataframe.values
X = dataset[:, 0:4].astype(float)
Y = dataset[:, 4]


# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (one hot encoding)
dummy_y = np_utils.to_categorical(encoded_Y)
 
# define model structure
def baseline_model():
    model = Sequential()
    model.add(Dense(8, activation="relu", input_dim=4))
    model.add(Dropout(0.2))
    model.add(Dense(3, activation="softmax",input_dim=8))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=10) #, verbose=0
# splitting data into training set and test set. If random_state is set to an integer, the split datasets are fixed.
X_train, X_test, Y_train, Y_test = train_test_split(X, dummy_y, test_size=0.3, random_state=0)
estimator.fit(X_train, Y_train)
 
# make predictions
pred = estimator.predict(X_test)
# inverse numeric variables to initial categorical labels
init_lables = encoder.inverse_transform(pred)
# k-fold cross-validate
seed = 7
np.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))