#?Package?imports
import?pandas?as?pd
import?numpy?as?np
import?matplotlib.pyplot?as?plt

def?clc_accuracy（y_true?y_predict）:
????“““?use?sklearn?to?calcuate?the?R2?score“““
????from??sklearn.metrics?import?r2_score
????score?=?r2_score（y_true?y_predict）
????return?score

def?sigmoid（Z）:
????“““
????Compute?the?sigmoid?of?x
????Arguments:
????x?--?A?scalar?or?numpy?array?of?any?size.
????Return:
????s?--?sigmoid（x）
????“““
????A?=?1.0/（1.0+np.exp（-Z））
????return?A

def?sigmoid_backward（dA?cache）:
????“““
????Implement?the?backward?propagation?for?a?single?SIGMOID?unit.

????Arguments:
????dA?--?post-activation?gradient?of?any?shape
????cache?--?‘Z‘?where?we?store?for?computing?backward?propagation?efficiently

????Returns:
????dZ?--?Gradient?of?the?cost?with?respect?to?Z
????“““
????Z?=?cache
????s?=?1.0?/?（1.0?+?np.exp（-Z））
????dZ?=?dA?*?s?*?（1?-?s）
????assert?（dZ.shape?==?Z.shape）
????return?dZ

def?relu（Z）:
????“““
????Implement?the?RELU?function.
????Arguments:
????Z?--?Output?of?the?linear?layer?of?any?shape
????Returns:
????A?--?Post-activation?parameter?of?the?same?shape?as?Z
????cache?--?a?python?dictionary?containing?“A“?;?stored?for?computing?the?backward?pass?efficiently
????“““
????A?=?np.maximum（0?Z）
????assert?（A.shape?==?Z.shape）
????return?A

def?relu_backward（dA?cache）:
????“““
????Implement?the?backward?propagation?for?a?single?RELU?unit.
????Arguments:
????dA?--?post-activation?gradient?of?any?shape
????cache?--?‘Z‘?where?we?store?for?computing?backward?propagation?efficiently
????Returns:
????dZ?--?Gradient?of?the?cost?with?respect?to?Z
????“““
????Z?=?cache
????dZ?=?np.array（dA?copy=True）??#?just?converting?dz?to?a?correct?object.
????#?When?z?<=?0?you?should?set?dz?to?0?as?well.
????dZ[Z?<=?0]?=?0
????assert?（dZ.shape?==?Z.shape）
????return?dZ

def?layer_sizes（X?Y）:
????“““
????Arguments:
????X?--?input?dataset?of?shape?（input?size?number?of?examples）
????Y?--?labels?of?shape?（output?size?number?of?examples）

????Returns:
????n_x?--?the?size?of?the?input?layer
????n_h?--?the?size?of?the?hidden?layer
????n_y?--?the?size?of?the?output?layer
????“““
????n_x?=?X.shape[0]??#?size?of?input?layer?number?of?features??
????n_h?=?4
????n_y?=?Y.shape[0]??#?size?of?output?layer
????return?（n_x?n_h?n_y）


def?initialize_parameters（n_x?n_h?n_y）:
????“““
????Argument:
????n_x?--?size?of?the?input?layer
????n_h?--?size?of?the?hidden?layer
????n_y?--?size?of?the?output?layer
????Returns:
????params?--?python?dictionary?containing?your?parameters:
????W1?--?weight?matrix?of?shape?（n_h?n_x）
????b1?--?bias?vector?of?shape?（n_h?1）
????W2?--?weight?matrix?of?shape?（n_y?n_h）
????b2?--?bias?vector?of?shape?（n_y?1）
????“““
????np.random.seed（2）??#?we?set?up?a?seed?so?that?your?output?matches?ours?although?the?initialization?is?random.

????W1?=?np.random.randn（n_h?n_x）?*?0.01
????b1?=?np.zeros（（n_h?1））
????W2?=?np.random.r

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件???????11828??2018-08-29?08:47??pred_nn.py
?????文件???????12435??2018-04-28?00:36??housing.csv