import numpy as np 

class LR_Classifier():
    def __init__(self, inputs_dim=None):
        self.Linear1 = np.random.normal(size=inputs_dim+1)
        print("linear shape: ", self.Linear1.shape)
    def forward(self, x):
        # print("inputs shape: ", x.shape)
        # batch_size = x.shape[0]
        # print("inptus: ", x)
        a = np.dot(x,self.Linear1)
        # print("logit: ", a)
        self.outputs = 1/(1+np.exp(-a))
        self.predictions = self.outputs > 0.5
        # print("predictions shape: ", predictions.shape)
        return self.outputs, self.predictions



    
14b93ac6c1f8bbf901523719dee694da2279b788
<mask token>


class LR_Classifier:
    <mask token>
    <mask token>

<mask token>


class LR_Classifier:

    def __init__(self, inputs_dim=None):
        self.Linear1 = np.random.normal(size=inputs_dim + 1)
        print('linear shape: ', self.Linear1.shape)
    <mask token>

<mask token>


class LR_Classifier:

    def __init__(self, inputs_dim=None):
        self.Linear1 = np.random.normal(size=inputs_dim + 1)
        print('linear shape: ', self.Linear1.shape)

    def forward(self, x):
        a = np.dot(x, self.Linear1)
        self.outputs = 1 / (1 + np.exp(-a))
        self.predictions = self.outputs > 0.5
        return self.outputs, self.predictions

import numpy as np


class LR_Classifier:

    def __init__(self, inputs_dim=None):
        self.Linear1 = np.random.normal(size=inputs_dim + 1)
        print('linear shape: ', self.Linear1.shape)

    def forward(self, x):
        a = np.dot(x, self.Linear1)
        self.outputs = 1 / (1 + np.exp(-a))
        self.predictions = self.outputs > 0.5
        return self.outputs, self.predictions

import numpy as np 

class LR_Classifier():
    def __init__(self, inputs_dim=None):
        self.Linear1 = np.random.normal(size=inputs_dim+1)
        print("linear shape: ", self.Linear1.shape)
    def forward(self, x):
        # print("inputs shape: ", x.shape)
        # batch_size = x.shape[0]
        # print("inptus: ", x)
        a = np.dot(x,self.Linear1)
        # print("logit: ", a)
        self.outputs = 1/(1+np.exp(-a))
        self.predictions = self.outputs > 0.5
        # print("predictions shape: ", predictions.shape)
        return self.outputs, self.predictions



    