需要用到的python庫有:
sklearn、tensorflow
import pandas as pd
import tensorflow as tf
from sklearn import datasets
# 載入資料集
iris_df = datasets.load_iris()
# 資料集切片
data = iris_df.data
data=pd.dataframe(data,columns=['seqpallength','sepalwidth','petallength','petalwidth'])
data['species']=iris_df.target
# 打亂資料集的順序
data = data.sample(frac=1).reset_index(drop=true)
# 把訓練集與測試集分開
data_train,data_test = data[:120],data[120:]
train_x, train_y = data_train, data_train.pop('species')
test_x,test_y = data_test,data_test.pop('species')
# 提取特徵值
feature_columns =
for key in train_x.keys():
print(feature_columns)
# 構建訓練模型
classifier = tf.estimator.dnnclassifier(
# 這個模型接受哪些輸入的特徵
feature_columns=feature_columns,
# 包含兩個隱藏層,每個隱藏層包含10個神經元.
hidden_units=[10, 10],
# 最終結果要分成幾類
n_classes=3)
def train_func(train_x,train_y):
dataset=tf.data.dataset.from_tensor_slices((dict(train_x), train_y))
dataset = dataset.shuffle(1000).repeat().batch(100)
return dataset
# 進行模型訓練,進行2000 個回合的訓練,每次100調資料
classifier.train(
input_fn=lambda:train_func(train_x,train_y),
steps=2000)
# 模型**
def eval_input_fn(features, labels, batch_size):
features=dict(features)
if labels is none:
# no labels, use only features.
inputs = features
else:
inputs = (features, labels)
dataset = tf.data.dataset.from_tensor_slices(inputs)
assert batch_size is not none, "batch_size must not be none"
dataset = dataset.batch(batch_size)
return dataset
predict_arr =
predictions = classifier.predict(
input_fn=lambda:eval_input_fn(test_x,labels=test_y,batch_size=100))
for predict in predictions:
result = predict_arr == test_y
result1 = [w for w in result if w == true]
print("準確率為 %s"%str((len(result1)/len(result))))
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卡特蘭數 Catalan數
卡特蘭數 規定h 0 1,而h 1 1,h 2 2,h 3 5,h 4 14,h 5 42,h 6 132,h 7 429,h 8 1430,h 9 4862,h 10 16796,h 11 58786,h 12 208012,h 13 742900,h 14 2674440,h 15 969484...
Catalan數(卡特蘭數)
2012 04 12 21 08 13 標籤 卡特蘭數 原始出處 作者資訊和本宣告。否則將追究法律責任。卡特蘭數 規定h 0 1,而h 1 1,h 2 2,h 3 5,h 4 14,h 5 42,h 6 132,h 7 429,h 8 1430,h 9 4862,h 10 16796,h 11 58...