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def backprop_this_layer(self, da_curr, z_curr, W_curr, b_curr, A_prev, activation_function):
  if activation_function is 'sigmoid':
    activation_back = self.sigmoid_backward 
  elif activation_function is 'relu':
    activation_back = self.relu_backward
  else:
    return
  m = A_prev.shape[1]
  
  dz_curr = activation_back(da_curr, z_curr)

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def forward_prop_this_layer(self, A_prev, W_curr, b_curr, activation_function):
    z_curr = np.dot(W_curr, A_prev) + b_curr

    if activation_function is 'relu':
      activation = relu
    elif activation_function is 'sigmoid':
      activation = sigmoid
    else:
      raise Exception(f"{activation_function} is currently not supported, Only sigmoid, relu are supported")

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def initialize_params(self, architecture):
    # We'll save parameters in a dictionary so that, we can acess them later on
    params = {} 
    for id_, layer in enumerate(architecture):
      # We're starting our layer from 1, There's a reason for this, think about it.
      layer_id = id_ + 1
      # With help of architecture provided, we'll get dimensions for each layer.
      input_dim = layer['input_dim']
      output_dim = layer['output_dim']
      

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NN_ARCHITECTURE = [
    {"input_dim": 3, "output_dim": 4, "activation": "relu"},# First Hidden Layer
    {"input_dim": 4, "output_dim": 4, "activation": "relu"},# Second Hidden Layer 
    {"input_dim": 4, "output_dim": 1, "activation": "sigmoid"},# Output Layer
]
    

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import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.datasets import make_blobs
# data is a tuple with 2 columns, one is the dataset that we made, and second is the labels representing centre.
data = make_blobs(n_samples = 200, n_features=2, centers=3, cluster_std=1.5)

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(data[0])

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CodeHeat 2017-18

• Name : Aditya Kaushik
• Github : aditya98ak
• Twitter : adity98ak
• Facebook : AdityaKaushik001

Code Contributions:

I have worked on following projects in this contest :