def parse_to_numpy(pd):
images_idx = []
for string in pd[0]:
images_idx.append(get_num(string))
pd.insert(1, "Image_Index", images_idx, True)
pd = pd.sort_values(by=['Image_Index'])
pd = pd.reset_index(drop=True)
del pd['Image_Index']
del pd[0]
np = pd.to_numpy()
return np
def main():
debug_def = '2'
if debug_def == '':
### Preparing Data ###
x_train, y_train, x_test, y_test = preparing_trained_data()
print(x_train[0], y_train[0])
### Createing Model ###
model = creating_model()
### Training Data ###
train_history = training_model(model, x_train, y_train)
show_train_history(train_history, 'acc', 'val_acc')
show_train_history(train_history, 'loss', 'val_loss')
### Saving Data ###
saving_model(model, model_name)
### Evaluating Data ###
evaluating_model(model, x_test, y_test)
return
pass
elif debug_def == '2':
### Preparing Data ###
x_train, y_train, x_test, y_test = preparing_trained_data()
model = loading_model(model_name)
### Predicting Data ###
all_Features, Label = PreprocessData(all_df)
all_probability = model.predict(all_Features)
pd = all_df
pd.insert(len(all_df.columns), 'probability', all_probability)
print(pd[-2:])
pass
pass
# 评估模型准确率
scores = model.evaluate(x=test_features, y=test_label)
print(scores[1])
print(scores[0])
# 加入Jack 和 Rose的数据
Jack = pd.Series([0, 'Jack', 3, 'male', 23, 1, 0, 5.0000, 'S'])
Rose = pd.Series([1, 'Rose', 1, 'female', 20, 1, 0, 100.0000, 'S'])
# 创建 Pandas DataFramen JR_df, 加入jack和rose数据
JR_df = pd.DataFrame([list(Jack), list(Rose)],
columns=[
'survived', 'name', 'pclass', 'sex', 'age', 'sibsp',
'parch', 'fare', 'embarked'
])
all_df2 = pd.concat([all_df, JR_df])
all_features, all_label = preprocess_data(all_df2)
all_probability = model.predict(all_features)
all_probability[-2:]
# 将 all_df 与 all_probability 整合
pd = all_df2
pd.insert(len(all_df2.columns), 'probability', all_probability)
JR = pd[-2:]
moving = pd[(pd['survived'] == 0) & (pd['probability'] > 0.9)]
print(model.summary())
model.compile(loss='categorical_crossentropy',
optimizer='adam', metrics=['accuracy'])
train_history=model.fit(x=Train4D_train_Features,
y=train_LabelOneHot,validation_split=0.2,
epochs=30,batch_size=100,verbose=2)
scores=model.evaluate(x=Train4D_train_Features,y=train_LabelOneHot)
print(model.summary())
print('accuracy=',scores[1])
"""all_Features,Label=PreprocessData(all_df)
all_probability=model.predict(all_Features)
pd=all_df
pd.insert(len(all_df.columns),'probability',all_probability)"""
import matplotlib.pyplot as plt
def show_train_history(train_history,train,validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel('accuracy')
plt.xlabel('Epoch')
plt.legend(['train','validation',],loc='upper left')
plt.show()
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
show_train_history(train_history, 'acc', 'val_acc')
show_train_history(train_history, 'loss', 'val_loss')
"""[Evaluate]"""
scores = model.evaluate(x_img_test_normalize, y_label_test_OneHot, verbose=1)
scores[1]
"""[Create forecast data]"""
#Series 輸入資料
Jack = pd.Series([0, 'Jack', 3, 'male', 23, 1, 0, 5.0000, 'S'])
Rose = pd.Series([1, 'Rose', 1, 'female', 20, 1, 0, 100.0000, 'S'])
#建立兩人DataFrame
JR_df = pd.DataFrame([list(Jack), list(Rose)],
columns=[
'survived', 'name', 'pclass', 'sex', 'age', 'sibsp',
'parch', 'fare', 'embarked'
])
#加入所有DataFrame會在最後兩個
all_df = pd.concat([all_df, JR_df])
#新增兩個人，所以feature & label要重新取
all_Features, Label = PreprocessData(all_df)
#預測，可得到每個乘客存活機率
all_probability = model.predict(all_Features)
#存活機率加入DataFrame
pd = all_df
pd.insert(len(all_df.columns), 'survive probability', all_probability)
"""[Find]"""
pd[(pd['survived'] == 0) & (pd['survive probability'] > 0.9)]
pd[:5]