def predict(X, w, b): # X Input. # Calculating presictions/y_hat. preds = sigmoid(np.dot(X, w) + b) # Empty List to s
Posted: Tue Apr 12, 2022 10:25 am
def predict(X, w, b):
# X Input.
# Calculating presictions/y_hat.
preds = sigmoid(np.dot(X, w) + b)
# Empty List to store predictions.
pred_class = []
# Delete the following two lines and replace it with your own
for i in preds:
pred_class.append(0)
# if y_hat >= 0.5 round up to 1
# if y_hat < 0.5 round down to 0
###
### YOUR CODE HERE
###
return np.array(pred_class)
Step 5 Extract only the x data def extract_only_x_data(dataset): if len(dataset) == 0: return data = list) for i in range(0, len(dataset)): data.append(list) for j in range(0, len (dataset) - 1): data[-1].append(float(dataset[j])) return data Step 6 Extract only the y data def extract_only_y_data(dataset): if len(dataset) return == @: data = list) for i in range(0, len(dataset)): data.append(int(dataset[-1])) return data
Step 7 1 Define sigmoid function def sigmoid(z): z = 1/(1 + np.exp(-z)) # Return the value of the implemented sigmoid function, do not simply return z return z Step 8 Define loss function def loss(y, y_hat): loss = - np.mean(y*(np.log(y_hat)) - (1-y) *np.log(1-y_hat)) return loss Step 9 Define gradients function def gradients(x, y, y_hat): # X Input. # y true/target value. # y_hat predictions. # w weights. # b bias. # number of training examples. numner_of_examples = X.shape[O] # Gradient of Loss weights. (1/numner_of_examples)*np.dot(X.T, (y_hat - y)) dw = # Gradient of Loss bias. (1/numner_of_examples)*np. sum((y_hat - y)) db = return dw, db
Step 10 Train the dataset def train(x, y, batch_size, epochs, learning_rate): # X Input. # y true/target value. # batch_size Batch Size. # epochs Number of iterations. # Learning_rate Learning rate. # number of training examples # number of features numner_of_examples, numner_of_features = X.shape # Initializing weights and bias to zeros. weights = np.zeros((numner_of_features,1)) bias = 0 # Reshaping y. y = y.reshape(numner_of_examples,1) # Empty list to store losses. losses = [] # Training Loop. for epoch in range(epochs): for i in range((numner_of_examples-1)//batch_size + 1): = # Defining batches. SGD. start_i - i * batch_size end_i = start_i + batch_size xb = X[start_i:end_i] yb = y[start_i:end_i] = # Calculating hypothesis/prediction. y_hat = sigmoid(np.dot(xb, weights) + bias) # Getting the gradients of loss w.r.t parameters. dw, db = gradients(xb, yb, y_hat)
# Updating the parameters. weights = weights - learning_rate dw bias = bias - learning_rate * db # Calculating loss and appending it in the list. 1 = loss(y, sigmoid(np.dot(X, weights) + bias)) losses.append(1) = # returning weights, bias and Losses(List). return weights, bias, losses
Make the prediction - def predict(X, W, b): # X Input. # Calculating presictions/y_hat. preds = sigmoid(np.dot(X, W) + b) # Empty List to store predictions. pred_class = [] # Delete the following two lines and replace it with your own for i in preds: pred_class.append() # if y_hat >= 0.5 round up to 1 # if y_hat < 0.5 round down to e ### YOUR CODE HERE 표표 return np.array(pred_class)
# X Input.
# Calculating presictions/y_hat.
preds = sigmoid(np.dot(X, w) + b)
# Empty List to store predictions.
pred_class = []
# Delete the following two lines and replace it with your own
for i in preds:
pred_class.append(0)
# if y_hat >= 0.5 round up to 1
# if y_hat < 0.5 round down to 0
###
### YOUR CODE HERE
###
return np.array(pred_class)
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Step 7 1 Define sigmoid function def sigmoid(z): z = 1/(1 + np.exp(-z)) # Return the value of the implemented sigmoid function, do not simply return z return z Step 8 Define loss function def loss(y, y_hat): loss = - np.mean(y*(np.log(y_hat)) - (1-y) *np.log(1-y_hat)) return loss Step 9 Define gradients function def gradients(x, y, y_hat): # X Input. # y true/target value. # y_hat predictions. # w weights. # b bias. # number of training examples. numner_of_examples = X.shape[O] # Gradient of Loss weights. (1/numner_of_examples)*np.dot(X.T, (y_hat - y)) dw = # Gradient of Loss bias. (1/numner_of_examples)*np. sum((y_hat - y)) db = return dw, db
Step 10 Train the dataset def train(x, y, batch_size, epochs, learning_rate): # X Input. # y true/target value. # batch_size Batch Size. # epochs Number of iterations. # Learning_rate Learning rate. # number of training examples # number of features numner_of_examples, numner_of_features = X.shape # Initializing weights and bias to zeros. weights = np.zeros((numner_of_features,1)) bias = 0 # Reshaping y. y = y.reshape(numner_of_examples,1) # Empty list to store losses. losses = [] # Training Loop. for epoch in range(epochs): for i in range((numner_of_examples-1)//batch_size + 1): = # Defining batches. SGD. start_i - i * batch_size end_i = start_i + batch_size xb = X[start_i:end_i] yb = y[start_i:end_i] = # Calculating hypothesis/prediction. y_hat = sigmoid(np.dot(xb, weights) + bias) # Getting the gradients of loss w.r.t parameters. dw, db = gradients(xb, yb, y_hat)
# Updating the parameters. weights = weights - learning_rate dw bias = bias - learning_rate * db # Calculating loss and appending it in the list. 1 = loss(y, sigmoid(np.dot(X, weights) + bias)) losses.append(1) = # returning weights, bias and Losses(List). return weights, bias, losses
Make the prediction - def predict(X, W, b): # X Input. # Calculating presictions/y_hat. preds = sigmoid(np.dot(X, W) + b) # Empty List to store predictions. pred_class = [] # Delete the following two lines and replace it with your own for i in preds: pred_class.append() # if y_hat >= 0.5 round up to 1 # if y_hat < 0.5 round down to e ### YOUR CODE HERE 표표 return np.array(pred_class)