API-Sequential Model-Optimizer

import keras
# data processing X , y 
# 載入 iris data
from sklearn import datasets
import numpy as np
iris = datasets.load_iris()
X = iris['data'].astype(np.float32)      #X = iris.get('data')
y = iris['target']                       #y = iris.get('target')
y[-3:]

# check X data type
X.dtype

from keras.utils import to_categorical
y_oh = to_categorical(y)
y_oh[:10]

# 分出訓練 跟 驗證
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y_oh, test_size=0.2, random_state=42)

# 建構神經層
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()        # https://keras.io/activations/
model.add(Dense(units=1000, activation='relu', input_shape=(4,) ) )
                            # softmax 用在最後一層輸出分類的時候計算最後屬於哪一個神經層
model.add(Dense(units=3, activation='softmax'))
model.summary()

# categorical_crossentropy 表示分類正確 越小越好
model.compile(optimizer='adam' , loss='categorical_crossentropy', metrics=['accuracy'] )
history = model.fit(X_train, y_train, validation_split=0.2, batch_size=2, epochs=20, verbose=1)
# loss , accuracy
model.evaluate(X_test, y_test)
#視覺化結果
import matplotlib.pyplot as plt
history.history.keys()
%matplotlib inline
plt.plot(history.history['loss'], color='red')
plt.plot(history.history['val_loss'], color='blue')
plt.show()
%matplotlib inline
plt.plot(history.history['acc'], color='red')
plt.plot(history.history['val_acc'], color='blue')
plt.show()