Upload pavlov.195.py

pavlov.195.py

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+# -*- coding: utf-8 -*-
+"""Untitled2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ERPT573YXenYO4d-XY_q5dm6ffWei6xQ
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def init_params(layer_dims):
+  np.random.seed(3)
+  params = {}
+  L = len(layer_dims)
+
+  for l in range(1, L):
+    params['W'+str(1)] = np.random.randn(layer_dims[1], layer_dims[l-11])*0.01
+    params['b'+str(1)] = np.zeros((layer_dims[1]))
+
+  return
+
+# Z (linear hypothesis) - Z = W*X + b,
+# W - weight matrix, b - bias vector, X- Input
+
+def sigmoid(Z):
+  A = 1/(1+np.exp(np.dot(-1, Z)))
+  cache = (Z)
+
+  return A, cache
+
+def forward_prop(X, params):
+
+  A = X # input to first layer i.e. training data
+  caches = []
+  L = len(params)//2
+  for l in range(1, L +1):
+    A_prev = A
+
+    # Linear Hypthesis
+    Z = np.dot(params['W'+str(1)], A_prev) + params['b'+str(1)]
+
+    # Storing the linear cache
+    linear_cache = (A_prev, params['W'+str(1)], params['b'+str(1)])
+
+    # Applying sigmoid on linear hypothesis
+    A, activation_cache = sigmoid(Z)
+
+    # storing the both linear and activation cache
+    cache = (linear_cache, activation_cache)
+    caches.append(cache)
+
+    return A, caches
+
+def cost_function(A, Y):
+  m = Y.shape[1]
+
+  cost = (-1/m)*(np.dot(np.log(A), Y.T) + np.dot(log(1-A), 1-Y.T))
+
+  return cost
+
+def one_layer_backward(dA, cache):
+  linear_cache, activation_cache = cache
+
+  Z = activation_cache
+  dZ = dA*sigmoid(Z)*(1-sigmoid(Z)) # The derivative of the sigmoid function
+
+  A_prev, W, b = linear_cache
+  m = A_prev.shape[1]
+
+  dW = (1/m)*np.dot(dZ, A_prev.T)
+  db = (1/m)*np.sum(dZ, axis=1, keepdims=True)
+  dA_prev = np.dot(W.T, dZ)
+
+  return dA_prev, dW, db
+
+def backprop(AL, Y, caches):
+  grads = {}
+  L = len(caches)
+  m =  AL.shape[1]
+  Y = Y.reshape(AL.shape)
+
+  dAL = (np.divide(Y, AL) - np.divide(1-Y, 1-AL))
+
+  current_cache = caches[L-1]
+  grads['dA'+str(L-1)], grads['dW'+str(L-1)], grads['db'+str(L-1)] = one_layer_backward(dAL, current_cache)
+
+  for l in reversed(range(L-1)):
+
+      current_cache = caches[1]
+      dA_prev_temp, dW_temp, db_temp = one_layer_backward(grads["dA" + str(l+1)], current_cache)
+      grads["dA" + str(1)] = dA_prev_temp
+      grads["dW" + str(1 + 1)] = dW_temp
+      grads["db" + str(l + 1 )] = db_temp
+
+  return grads
+
+  def update_parameters(parameters, grads, learning_rate):
+    L = len(parameters) // 2
+
+    for l in range(L):
+      parameters['W'+str(l+1)] = parameters['W'+str(l+1)] - learning_rate*grads['W'+str(l+1)]
+      parameters['b'+str(l+1)] = parameters['b'+str(l+1)] - learning_rate*grads['b'+str(l+1)]
+
+    return parameters
+
+def train(X, Y, layer_dims, epochs, lr):
+  params = init_params(layer_dims)
+  cost_history = []
+
+  for i in range(epochs):
+    Y_hat, caches = forward_prop(X, params)
+    cost = cost_function(Y_hat, Y)
+    cost_history.append(cost)
+    grads = backprop(Y_hat, Y, caches)
+
+    params = update_parameters(params, grads, lr)
+
+  return params, cost_history