depp_learning

Author: Y1ran

Diff for: Deep_learning/propagate.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Aug 11 16:14:09 2018
+
+@author: Administrator
+"""
+
+def propagate(w, b, X, Y):
+    """
+    Implement the cost function and its gradient for the propagation explained above
+
+    Arguments:
+    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
+    b -- bias, a scalar
+    X -- data of size (num_px * num_px * 3, number of examples)
+    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)
+
+    Return:
+    cost -- negative log-likelihood cost for logistic regression
+    dw -- gradient of the loss with respect to w, thus same shape as w
+    db -- gradient of the loss with respect to b, thus same shape as b
+    
+    Tips:
+    - Write your code step by step for the propagation. np.log(), np.dot()
+    """
+    m = X.shape[1]
+    
+    # FORWARD PROPAGATION (FROM X TO COST)
+    ### START CODE HERE ### (≈ 2 lines of code)
+    A = sigmoid(np.dot(X.T ,w) + b)                                  # compute activation
+    cost = -1 / m * (np.dot(Y,np.log(A)) + np.dot((1 - Y), np.log(1 - A)))                           # compute cost
+    ### END CODE HERE ###
+    
+    # BACKWARD PROPAGATION (TO FIND GRAD)
+    ### START CODE HERE ### (≈ 2 lines of code)
+    import math
+    dw = 1 / m * np.dot(X, (A - Y.T))
+    db = 1 / m * np.sum((A - Y.T).T)
+    ### END CODE HERE ###
+
+    assert(dw.shape == w.shape)
+    assert(db.dtype == float)
+    cost = np.squeeze(cost)
+    assert(cost.shape == ())
+    
+    grads = {"dw": dw,
+             "db": db}
+    
+    return grads, cost