import?matplotlib.pyplot?as?plt
import?h5py
import?sklearn
import?sklearn.datasets
import?sklearn.linear_model
import?scipy.io
import?numpy?as?np
np.seterr（divide=‘ignore‘?invalid=‘ignore‘）


plt.rcParams[‘figure.figsize‘]?=?（7.0?4.0）?#?set?default?size?of?plots
plt.rcParams[‘image.interpolation‘]?=?‘nearest‘
plt.rcParams[‘image.cmap‘]?=?‘gray‘

def?load_2D_dataset（）:
????data?=?scipy.io.loadmat（‘datasets/data.mat‘）
????train_X?=?data[‘X‘].T
????train_Y?=?data[‘y‘].T
????test_X?=?data[‘Xval‘].T
????test_Y?=?data[‘yval‘].T

????plt.scatter（train_X[0?:]?train_X[1?:]?c=train_Y[0?:]?s=40?cmap=plt.cm.Spectral）;
????plt.show（）

????return?train_X?train_Y?test_X?test_Y

def?print_size（）:
????train_X_size?=?print（train_X.shape）?#（2?211）
????train_Y_size?=?print（train_Y.shape）?#（1?211）
????test_X_size?=?print（test_X.shape）?#（2?200）
????test_Y_size?=?print（test_Y.shape）?#（1?200）

????return?train_X_size?train_Y_size?test_X_size?test_Y_size

#train_X?train_Y?test_X?test_Y?=?load_2D_dataset（）
#train_X_size?train_Y_size?test_X_size?test_Y_size?=?print_size（）

def?sigmoid（x）:
????“““
????Compute?the?sigmoid?of?x

????Arguments:
????x?--?A?scalar?or?numpy?array?of?any?size.

????Return:
????s?--?sigmoid（x）
????“““
????s?=?1?/?（1?+?np.exp（-x））
????return?s


def?relu（x）:
????“““
????Compute?the?relu?of?x

????Arguments:
????x?--?A?scalar?or?numpy?array?of?any?size.

????Return:
????s?--?relu（x）
????“““
????s?=?np.maximum（0?x）

????return?s

#類似He?et?al?Initialize
def?initialize_parameters（layer_dims）:
????“““
????Arguments:
????layer_dims?--?python?array?（list）?containing?the?dimensions?of?each?layer?in?our?network

????Returns:
????parameters?--?python?dictionary?containing?your?parameters?“W1“?“b1“?...?“WL“?“bL“:
????????????????????W1?--?weight?matrix?of?shape?（layer_dims[l]?layer_dims[l-1]）
????????????????????b1?--?bias?vector?of?shape?（layer_dims[l]?1）
????????????????????Wl?--?weight?matrix?of?shape?（layer_dims[l-1]?layer_dims[l]）
????????????????????bl?--?bias?vector?of?shape?（1?layer_dims[l]）

????Tips:
????-?For?example:?the?layer_dims?for?the?“Planar?Data?classification?model“?would?have?been?[221].
????This?means?W1‘s?shape?was?（22）?b1?was?（12）?W2?was?（21）?and?b2?was?（11）.?Now?you?have?to?generalize?it!
????-?In?the?for?loop?use?parameters[‘W‘?+?str（l）]?to?access?Wl?where?l?is?the?iterative?integer.
????“““

????np.random.seed（3）
????parameters?=?{}
????L?=?len（layer_dims）?#?number?of?layers?in?the?network

????for?l?in?range（1?L）:
????????parameters[‘W‘?+?str（l）]?=?np.random.randn（layer_dims[l]?layer_dims[l?-?1]）?/?np.sqrt（layer_dims[l?-?1]）
????????parameters[‘b‘?+?str（l）]?=?np.zeros（（layer_dims[l]?1））

????????#assert?（parameters[‘W‘?+?str（l）].shape?==?layer_dims[l]?layer_dims[l?-?1]）
????????#assert?（parameters[‘W‘?+?str（l）].shape?==?layer_dims[l]?1）

????return?parameters


def?forward_propagation（X

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件?????2545837??2018-12-25?14:16??20181215Python-無正則化、L2正則化與Dropout.mp4
?????目錄???????????0??2018-12-25?14:29??datasets\
?????文件????????6038??2017-09-03?18:02??datasets\data.mat
?????文件???????19889??2018-12-25?11:16??Regularization_French?Football_3NN.py