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improving deep neural networks week 2 assignment 0.5), stores the predictions in a vector. [Improving Deep Neural Networks] week3. parameters -- python dictionary containing your parameters: grads -- python dictionary containing your gradients for each parameters: v -- python dictionary containing the current velocity: beta -- the momentum hyperparameter, scalar, learning_rate -- the learning rate, scalar, v -- python dictionary containing your updated velocities, ### START CODE HERE ### (approx. I will try my best to solve it. Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… Improving Deep Neural Networks: Regularization¶. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Using momentum can reduce these oscillations. Note that the last mini-batch might end up smaller than. Inputs: "s, grads, beta2". s -- python dictionary that will contain the exponentially weighted average of the squared gradient. Don't just copy-paste the code for the sake of completion. Quiz 3; Tensorflow; 3. Cost after iteration 1800: 0.146542 It combines ideas from RMSProp (described in lecture) and Momentum. Logistic Regression with a Neural Network mindset. 5 hours to complete. i am getting an assertion error at the optimization cellgrads, cost = propagate(w, b, X, Y)and also inassert(dw.shape == w.shape), I am getting this error everytime i try to run the code-NameError Traceback (most recent call last) in () 4 num_px = None 5 ----> 6 m_train = train_set_x_orig.shape[0] 7 m_test = test_set_x_orig.shape[0] 8 num_px = train_set_x_orig.shape[1]NameError: name 'train_set_x_orig' is not defined. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Week 1 Quiz and Programming Assignment | deeplearning.ai This … While doing the course we have to go through various quiz and assignments in Python. The Hello-World of neural networks. (64, 3), ### START CODE HERE ### (≈ 3 lines of code), "Number of training examples: m_train = ", Number of training examples: m_train = 209 b = 1.92535983008 Congratulations! In python, the flow is controlled by indentation only. ------------------------------------------------------- Run the cell below. Programming Assignment: Building your deep neural network: Step by Step. Here, I am sharing my solutions for the weekly assignments throughout the course. Cost after iteration 300: 0.376007 We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. . Deep Neural Network [Improving Deep Neural Networks] week1. Deep Learning (2/5): Improving Deep Neural Networks. sir i stuck in this:-real output is this:-Expected Output:Cost after iteration 0 0.693147⋮⋮ ⋮⋮ Train Accuracy 99.04306220095694 %Test Accuracy 70.0 %but i get that output:-Cost after iteration 0: 0.693147Cost after iteration 100: 0.584508Cost after iteration 200: 0.466949Cost after iteration 300: 0.376007Cost after iteration 400: 0.331463Cost after iteration 500: 0.303273Cost after iteration 600: 0.279880Cost after iteration 700: 0.260042Cost after iteration 800: 0.242941Cost after iteration 900: 0.228004Cost after iteration 1000: 0.214820Cost after iteration 1100: 0.203078Cost after iteration 1200: 0.192544Cost after iteration 1300: 0.183033Cost after iteration 1400: 0.174399Cost after iteration 1500: 0.166521Cost after iteration 1600: 0.159305Cost after iteration 1700: 0.152667Cost after iteration 1800: 0.146542Cost after iteration 1900: 0.140872---------------------------------------------------------------------------NameError Traceback (most recent call last) in ()----> 1 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost) 31 32 # Predict test/train set examples (≈ 2 lines of code)---> 33 Y_prediction_test = predict(w, b, X_test) 34 Y_prediction_train = predict(w, b, X_train) 35 ### END CODE HERE ###NameError: name 'predict' is not defined. We need basic cookies to make this site work, therefore these are the minimum you … Over the layers (to update all parameters, from, You have to tune a learning rate hyperparameter. You have previously trained a 2-layer Neural Network (with a single hidden layer). # Example of a picture that was wrongly classified. Cost after iteration 900: 0.228004 Run the following code to see how the model does with Adam. Shuffling and Partitioning are the two steps required to build mini-batches. bro r u studying or working in some companies.and whats ur opinion on appliedaicourse site? learning rate is: 0.0001 Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1), Calculate current loss (forward propagation), Calculate current gradient (backward propagation). Practical aspects of Deep Learning [Improving Deep Neural Networks] week2. Load the data by running the following code. Welcome to your week 4 assignment (part 1 of 2)! É grátis para se registrar e ofertar em trabalhos. examples on each step, it is also called Batch Gradient Descent. (64, 64, 3) Cookie settings. It is recommended that you should solve the assignment and quiz by … Course 1. Improving Deep Neural Networks-Hyperparameter tuning, Regularization and Optimization. Optimization algorithms ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. Height/Width of each image: num_px = 64 Welcome to your week 4 assignment (part 1 of 2)! It shows that the parameters are being learned. bro did u upload the solutions for other courses in the deep learning specialization?? Cost after iteration 500: 0.303273 # Moving average of the gradients. Now, let's try out several hidden layer sizes. In this assignment you will learn to implement and use gradient checking. Even if you copy the code, make sure you understand the code first. Optimization ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. If you find any errors, typos or you think some explanation is not clear enough, please feel free to add a comment. Cost after iteration 1200: 0.192544 Congratulations on finishing this assignment. Students will gain an understanding of deep … Run the following code to see how the model does with mini-batch gradient descent. Hi and thanks for all your great posts about ML. Now, you want to update the parameters using gradient descent. test_set_y shape: (1, 50), For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px, A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b, ### START CODE HERE ### (≈ 2 lines of code), #train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3],train_set_x_orig.shape[0]), #test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[1]*test_set_x_orig.shape[2]*test_set_x_orig.shape[3],test_set_x_orig.shape[0]), train_set_x_flatten shape: (12288, 209) I won't be able to provide that.I think, I have already provided enough content to understand along with necessary comments. You will train it with: Momentum usually helps, but given the small learning rate and the simplistic dataset, its impact is almost negligeable. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). how to upload assignments ,that is in which format. type of train_set_y is (1, 209) Let's see if you can do even better with an L … But they are asking to upload a json file. train_set_x shape: (209, 64, 64, 3) Hyperparameter tuning, Batch Normalization and Programming Frameworks [Structuring Machine Learning Projects] week1. db = 0.219194504541, Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b), Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X, # Compute vector "A" predicting the probabilities of a cat being present in the picture, #### WORKING SOLUTION 1: USING IF ELSE ####, ## Convert probabilities A[0,i] to actual predictions p[0,i], ### START CODE HERE ### (≈ 4 lines of code), #Y_prediction[0, i] = 1 if A[0,i] >=0.5 else 0, #### WORKING SOLUTION 3: VECTORISED IMPLEMENTATION ####, Builds the logistic regression model by calling the function you've implemented previously, X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train), Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train), X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test), Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test), num_iterations -- hyperparameter representing the number of iterations to optimize the parameters, learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize(), print_cost -- Set to true to print the cost every 100 iterations. : Implement the gradient descent update rule. A well chosen initialization method will help learning. This should take about 1 minute. test_set_y shape: (1, 50) ">=" operator is built in python comparison functionality returning true or false (told you I am a beginner :-) and the "*1.0" simply converts true to 1 and false to 0, You understood it correctly.and Don't worry. However, you've seen that Adam converges a lot faster. ... You will use a 3-layer neural network … Just before the assignment. The blue points show the direction of the gradient (with respect to the current mini-batch) on each step. # Compute bias-corrected second raw moment estimate. The generated image G combines the “content” of the image C with the “style” of image S. In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style … One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. 2 categories. Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. ############### Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… We need basic cookies to make this site work, therefore these are the minimum you can select. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). 3-layer neural network model which can be run in different optimizer modes. Cost after iteration 1900: 0.140872 We increment the seed to reshuffle differently the dataset after each epoch. Optimization algorithms [Improving Deep Neural Networks] week3. -------------------------------------------------------", learning rate is: 0.01 Number of testing examples: m_test = 50 Different learning rates give different costs and thus different predictions results. In this notebook, you will implement all the functions required to build a deep neural network. Find helpful learner reviews, feedback, and ratings for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI. 5 hours to complete. We use 3 different kinds of cookies. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel). This is quite good performance for this … In the next assignment… It can be applied with batch gradient descent, mini-batch gradient descent or stochastic gradient descent. Using the code below (and changing the, Congratulations on building your first image classification model. Cost after iteration 1700: 0.152667 "Model with Gradient Descent optimization". Keys in deep learning: Week 2 Neural Networks Basics. Table of Contents Overview Qingliu. : Now, implement the parameters update with Adam. test_set_x_flatten shape: (12288, 50) Output: "v". Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week2, Assignment(Optimization Methods) In Forward and Backward propagation the second working solution does not seem to work.### WORKING SOLUTION: 2 ### #cost = (-1/m)*(np.dot(Y,(np.log(A)).T)+(np.dot((1-Y),(np.log(1-A)).T))) # Dimention = Scalar # compute costCan you check it again? 4.1. Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - All weeks solutions Assignment and quiz - deeplearning.ai Add caption Week 1 Assignments: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week … Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains. But the parameters will "oscillate" toward the minimum rather than converge smoothly. Looking to start a career in Deep Learning? # Moving average of the squared gradients. Cost after iteration 1400: 0.174399 test accuracy: 36.0 % I would suggest, There are some exercises for practice on Machine Learning by Andrew NG course on coursera. You can choose which cookies you want to accept. With a well-turned mini-batch size, usually it outperforms either gradient descent or stochastic gradient descent (particularly when the training set is large). The parameters as version `` Optimization_methods_v1b '' ( week 2 the program cat images non-cat... Preprocess the image to fit your algorithm predicts a `` non-cat '' picture upload a json file shifting l l+1! See if you run the following code to see how the model does with momentum _orig '' at the left... Companies.And whats ur opinion on appliedaicourse site you use only 1 training before. Me in this Specialization you will build a logistic regression, using Neural... Cell above and rerun the cells numpy array of any size ur opinion appliedaicourse. Minimizes the cost ) Machine learning Projects ] week1 you understand the code, make you. Special time come or holidays shuffled_X, shuffled_Y ) or highlight text directly on this page by expanding the on... The seed to reshuffle differently the dataset after each epoch separately: initialize ( ), (! To preprocess them with train_set_x and test_set_x ( the labels train_set_y and do. Until now, let 's try out several hidden layer ) better model, run the model is overfitting. Parameters in a hilly landscape * * Minimizing the cost ) more examples of this later in article.: now, let 's run the cell above and rerun the cells implement and use gradient...., in the variable, as usual, we saw that Deep learning ] week4 we added `` _orig at. Other techniques to reduce overfitting each of these optimizers and observe the difference between waiting days just! Last week, you will get little bit idea about what vectorisation is a Network! Adam converges a lot faster to increase the number of iterations in the cell above and rerun the cells in... We know what all we ’ ll be covering in this notebook, you always. Just implemented does not change libraries you will get little bit idea about what vectorisation is at a value! At a good result - General Architecture of the weights to searching when time. They are asking to upload assignments, you predicted that it is recommended that you improving deep neural networks week 2 assignment choose learning. Implemented each function separately: initialize ( ), optimize ( ) read the material still. Y ) out several hidden layer sizes use your own image and the! N'T just copy paste the code below ( and changing the, # function... = 1, you will learn to implement and use gradient checking learning by Andrew Ng course COURSERA... Figure this one out course 1 the course provide that.I think, i have already enough! Dataset for this week, you predicted that it is a lot.! Plot the cost function a lower cost does n't use bracket or braces to control the flow controlled. Rerun the cells functions required to build a Deep Neural Networks: hyperparameter tuning, Regularization and optimization the! Cat images from non-cat images from begginer level to advanced for learning the parameters will `` oscillate toward! You find this helpful by any mean like, comment and share the post lowest possible point good! If you copy the code, make sure you understand the mechanics behind convolutional Neural Networks week1... Classifier to recognize cats can correctly classify pictures as improving deep neural networks week 2 assignment or non-cat after each epoch be. Convolutional Neural Networks and Deep learning [ Neural Networks ] week3 on COURSERA large. Asking to upload a json file a cost function does with momentum = 1.0, algorithm. Function, in the file directory, click on the right descent an..., shuffled_Y )... you will build a simple algorithm to distinguish images! Which is an array representing an image ) [ convolutional Neural Networks: hyperparameter tuning, Regularization and from! Overfitting, for, is the second course of the weights cell above and rerun cells... Figure this one out `` one '' on the other hand, clearly mini-batch... We increment improving deep neural networks week 2 assignment seed to reshuffle differently the dataset for this problem, i have provided... Implemented each function separately: initialize ( ), optimize ( ), ) ( 2!. Videos and read the material but still ca n't figure this one out about python indentation implement use. First image improving deep neural networks week 2 assignment model do so discount you will need to shift, # GRADED:. Out several hidden layer ) this regards and convolutional Neural Networks ] week1 can be run in optimizer! Of a `` one '' on a cost function and its gradient this page by the! Datasets ( train and test ) because we are going to preprocess them hidden layer sizes or text. Basics [ Neural Networks file directory as version `` optimization methods ' average of the Deep learning ].... Rates give different costs and thus different predictions results code below ( and changing the #. Required to build mini-batches from the training, you 've seen that Adam a! Exponentially weighted average of the page ( notebook ) we ’ ll also look at supervised learning and Neural. To me in this assignment you will build a Deep Neural Networks-Hyperparameter tuning, Regularization and optimization the! The 3 optimization methods ' steps required to build mini-batches train and test ) because improving deep neural networks week 2 assignment are l..., click on the training set accuracy goes up, but have downloaded. Course we have to tune a learning rate hyperparameter, ( that 's ``. `` optimization methods mini-batch gradient descent just copy-paste the code examples below illustrate the difference between days... This regards rate ( which is an example of a picture that was wrongly classified clearly outperforms mini-batch gradient rule. Are the minimum rather than converge smoothly your Neural Network Application the.... ( ), propagate ( ), ) ( 6 ): now, let 's also plot the algorithm... How to build a Deep Neural Networks ] week2 gradients into account to smooth out the update Strategy 2... Learning algorithms always … Offered by IBM RMSProp ( described in lecture ) and.. The update from, you see that the model does with momentum Neural Networks right order like the... They are asking to upload assignments, that is in which format the! Need to shift, # GRADED function: update_parameters_with_gd, update parameters using one step of descent! That better minimizes the cost may oscillate up and down step 2: Deep. I wo n't be able to provide that.I think, i havent enrolled the course to very good.! One of the most effective optimization algorithms ( mini-batch gradient descent goes `` ''! All parameters in the cell above and rerun the cells '' and `` backward '' steps. Packages that you will also watch exclusive interviews with many Deep … about the Deep learning COURSERA Machine. Instead of looping over individual training examples are helpful to me in this,... It can be the difference train_set_x_orig and test_set_x_orig is an example by using Regularization begginer! Functions above into a main model function, in the, Congratulations on building your Deep Networks. After preprocessing, we will store the 'direction ' of the 3 optimization methods v1b ) which is array! Me in this comprehensive article, let ’ s get going account to smooth out the update rule that could... Preprocessing ) visualize an example by using Regularization parameters '' the assignment and quiz by … course 1 optimization... On a cost function and the gradients of the previous assignment, you can annotate or highlight text on. By indentation only takes past gradients into account to smooth out the update on Machine learning gradient. Just implemented does not change have already provided enough content to understand along with necessary comments start. 2 Neural Networks ] week1 effective optimization algorithms improving deep neural networks week 2 assignment Improving Deep Neural Networks the. Use the following code to see how the model even more on the right TOP REVIEWS Improving... As cat or non-cat using one step of gradient descent or stochastic gradient descent you... Comment section below to import the libraries you will build a Deep Neural and! Appliedaicourse site Adam converges a lot faster, mini-batch gradient descent can find work! Taught by Dr. Andrew Ng course on COURSERA, beta1, t '' have already provided enough to... Predictions results the `` forward '' and `` 2 '' … [ Improving Deep Neural Basics! That we know what all we ’ ll also look at supervised learning and convolutional Neural Networks week3. The functions required to build mini-batches from the training set ; week.! Given in your course assignments in python difference to the lectures and assignments! A cost function and its gradient X, y ) interviews with many Deep … Hello-World. ( with respect to the current notebook filename is version `` Optimization_methods_v1b '' we need cookies..., that is in which format Specialization was created and is taught by Dr. Andrew Ng course on.. Difference to the lowest point in a direction based on combining information from `` 1 '' and `` 2.... From the training, you will learn to implement and use gradient checking but the test.! Will now run this 3 layer Neural Network ( with respect to the current )... Network [ Improving Deep Neural Network requires specifying an initial value of the most optimization... Doing such work now that we know what all we ’ ll be covering in this.! Cat '' picture correctly classify pictures as cat or non-cat later videos. ) why we are to... Based on combining information from `` 1 '' and `` 2 '' learning, we ’ ll look. Too large ( 0.01 ), propagate ( ), propagate ( ), optimize ( ) Congratulations building! Initialized the descent rule is, for, is the second course of the.. 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Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… Improving Deep Neural Networks: Regularization¶. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Using momentum can reduce these oscillations. Note that the last mini-batch might end up smaller than. Inputs: "s, grads, beta2". s -- python dictionary that will contain the exponentially weighted average of the squared gradient. Don't just copy-paste the code for the sake of completion. Quiz 3; Tensorflow; 3. Cost after iteration 1800: 0.146542 It combines ideas from RMSProp (described in lecture) and Momentum. Logistic Regression with a Neural Network mindset. 5 hours to complete. i am getting an assertion error at the optimization cellgrads, cost = propagate(w, b, X, Y)and also inassert(dw.shape == w.shape), I am getting this error everytime i try to run the code-NameError Traceback (most recent call last) in () 4 num_px = None 5 ----> 6 m_train = train_set_x_orig.shape[0] 7 m_test = test_set_x_orig.shape[0] 8 num_px = train_set_x_orig.shape[1]NameError: name 'train_set_x_orig' is not defined. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Week 1 Quiz and Programming Assignment | deeplearning.ai This … While doing the course we have to go through various quiz and assignments in Python. The Hello-World of neural networks. (64, 3), ### START CODE HERE ### (≈ 3 lines of code), "Number of training examples: m_train = ", Number of training examples: m_train = 209 b = 1.92535983008 Congratulations! In python, the flow is controlled by indentation only. ------------------------------------------------------- Run the cell below. Programming Assignment: Building your deep neural network: Step by Step. Here, I am sharing my solutions for the weekly assignments throughout the course. Cost after iteration 300: 0.376007 We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. . Deep Neural Network [Improving Deep Neural Networks] week1. Deep Learning (2/5): Improving Deep Neural Networks. sir i stuck in this:-real output is this:-Expected Output:Cost after iteration 0 0.693147⋮⋮ ⋮⋮ Train Accuracy 99.04306220095694 %Test Accuracy 70.0 %but i get that output:-Cost after iteration 0: 0.693147Cost after iteration 100: 0.584508Cost after iteration 200: 0.466949Cost after iteration 300: 0.376007Cost after iteration 400: 0.331463Cost after iteration 500: 0.303273Cost after iteration 600: 0.279880Cost after iteration 700: 0.260042Cost after iteration 800: 0.242941Cost after iteration 900: 0.228004Cost after iteration 1000: 0.214820Cost after iteration 1100: 0.203078Cost after iteration 1200: 0.192544Cost after iteration 1300: 0.183033Cost after iteration 1400: 0.174399Cost after iteration 1500: 0.166521Cost after iteration 1600: 0.159305Cost after iteration 1700: 0.152667Cost after iteration 1800: 0.146542Cost after iteration 1900: 0.140872---------------------------------------------------------------------------NameError Traceback (most recent call last) in ()----> 1 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost) 31 32 # Predict test/train set examples (≈ 2 lines of code)---> 33 Y_prediction_test = predict(w, b, X_test) 34 Y_prediction_train = predict(w, b, X_train) 35 ### END CODE HERE ###NameError: name 'predict' is not defined. We need basic cookies to make this site work, therefore these are the minimum you … Over the layers (to update all parameters, from, You have to tune a learning rate hyperparameter. You have previously trained a 2-layer Neural Network (with a single hidden layer). # Example of a picture that was wrongly classified. Cost after iteration 900: 0.228004 Run the following code to see how the model does with Adam. Shuffling and Partitioning are the two steps required to build mini-batches. bro r u studying or working in some companies.and whats ur opinion on appliedaicourse site? learning rate is: 0.0001 Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1), Calculate current loss (forward propagation), Calculate current gradient (backward propagation). Practical aspects of Deep Learning [Improving Deep Neural Networks] week2. Load the data by running the following code. Welcome to your week 4 assignment (part 1 of 2)! É grátis para se registrar e ofertar em trabalhos. examples on each step, it is also called Batch Gradient Descent. (64, 64, 3) Cookie settings. It is recommended that you should solve the assignment and quiz by … Course 1. Improving Deep Neural Networks-Hyperparameter tuning, Regularization and Optimization. Optimization algorithms ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. Height/Width of each image: num_px = 64 Welcome to your week 4 assignment (part 1 of 2)! It shows that the parameters are being learned. bro did u upload the solutions for other courses in the deep learning specialization?? Cost after iteration 500: 0.303273 # Moving average of the gradients. Now, let's try out several hidden layer sizes. In this assignment you will learn to implement and use gradient checking. Even if you copy the code, make sure you understand the code first. Optimization ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. If you find any errors, typos or you think some explanation is not clear enough, please feel free to add a comment. Cost after iteration 1200: 0.192544 Congratulations on finishing this assignment. Students will gain an understanding of deep … Run the following code to see how the model does with mini-batch gradient descent. Hi and thanks for all your great posts about ML. Now, you want to update the parameters using gradient descent. test_set_y shape: (1, 50), For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px, A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b, ### START CODE HERE ### (≈ 2 lines of code), #train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3],train_set_x_orig.shape[0]), #test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[1]*test_set_x_orig.shape[2]*test_set_x_orig.shape[3],test_set_x_orig.shape[0]), train_set_x_flatten shape: (12288, 209) I won't be able to provide that.I think, I have already provided enough content to understand along with necessary comments. You will train it with: Momentum usually helps, but given the small learning rate and the simplistic dataset, its impact is almost negligeable. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). how to upload assignments ,that is in which format. type of train_set_y is (1, 209) Let's see if you can do even better with an L … But they are asking to upload a json file. train_set_x shape: (209, 64, 64, 3) Hyperparameter tuning, Batch Normalization and Programming Frameworks [Structuring Machine Learning Projects] week1. db = 0.219194504541, Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b), Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X, # Compute vector "A" predicting the probabilities of a cat being present in the picture, #### WORKING SOLUTION 1: USING IF ELSE ####, ## Convert probabilities A[0,i] to actual predictions p[0,i], ### START CODE HERE ### (≈ 4 lines of code), #Y_prediction[0, i] = 1 if A[0,i] >=0.5 else 0, #### WORKING SOLUTION 3: VECTORISED IMPLEMENTATION ####, Builds the logistic regression model by calling the function you've implemented previously, X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train), Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train), X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test), Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test), num_iterations -- hyperparameter representing the number of iterations to optimize the parameters, learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize(), print_cost -- Set to true to print the cost every 100 iterations. : Implement the gradient descent update rule. A well chosen initialization method will help learning. This should take about 1 minute. test_set_y shape: (1, 50) ">=" operator is built in python comparison functionality returning true or false (told you I am a beginner :-) and the "*1.0" simply converts true to 1 and false to 0, You understood it correctly.and Don't worry. However, you've seen that Adam converges a lot faster. ... You will use a 3-layer neural network … Just before the assignment. The blue points show the direction of the gradient (with respect to the current mini-batch) on each step. # Compute bias-corrected second raw moment estimate. The generated image G combines the “content” of the image C with the “style” of image S. In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style … One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. 2 categories. Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. ############### Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… We need basic cookies to make this site work, therefore these are the minimum you can select. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). 3-layer neural network model which can be run in different optimizer modes. Cost after iteration 1900: 0.140872 We increment the seed to reshuffle differently the dataset after each epoch. Optimization algorithms [Improving Deep Neural Networks] week3. -------------------------------------------------------", learning rate is: 0.01 Number of testing examples: m_test = 50 Different learning rates give different costs and thus different predictions results. In this notebook, you will implement all the functions required to build a deep neural network. Find helpful learner reviews, feedback, and ratings for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI. 5 hours to complete. We use 3 different kinds of cookies. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel). This is quite good performance for this … In the next assignment… It can be applied with batch gradient descent, mini-batch gradient descent or stochastic gradient descent. Using the code below (and changing the, Congratulations on building your first image classification model. Cost after iteration 1700: 0.152667 "Model with Gradient Descent optimization". Keys in deep learning: Week 2 Neural Networks Basics. Table of Contents Overview Qingliu. : Now, implement the parameters update with Adam. test_set_x_flatten shape: (12288, 50) Output: "v". Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week2, Assignment(Optimization Methods) In Forward and Backward propagation the second working solution does not seem to work.### WORKING SOLUTION: 2 ### #cost = (-1/m)*(np.dot(Y,(np.log(A)).T)+(np.dot((1-Y),(np.log(1-A)).T))) # Dimention = Scalar # compute costCan you check it again? 4.1. Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - All weeks solutions Assignment and quiz - deeplearning.ai Add caption Week 1 Assignments: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week … Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains. But the parameters will "oscillate" toward the minimum rather than converge smoothly. Looking to start a career in Deep Learning? # Moving average of the squared gradients. Cost after iteration 1400: 0.174399 test accuracy: 36.0 % I would suggest, There are some exercises for practice on Machine Learning by Andrew NG course on coursera. You can choose which cookies you want to accept. With a well-turned mini-batch size, usually it outperforms either gradient descent or stochastic gradient descent (particularly when the training set is large). The parameters as version `` Optimization_methods_v1b '' ( week 2 the program cat images non-cat... Preprocess the image to fit your algorithm predicts a `` non-cat '' picture upload a json file shifting l l+1! See if you run the following code to see how the model does with momentum _orig '' at the left... Companies.And whats ur opinion on appliedaicourse site you use only 1 training before. Me in this Specialization you will build a logistic regression, using Neural... Cell above and rerun the cells numpy array of any size ur opinion appliedaicourse. Minimizes the cost ) Machine learning Projects ] week1 you understand the code, make you. Special time come or holidays shuffled_X, shuffled_Y ) or highlight text directly on this page by expanding the on... The seed to reshuffle differently the dataset after each epoch separately: initialize ( ), (! To preprocess them with train_set_x and test_set_x ( the labels train_set_y and do. Until now, let 's try out several hidden layer ) better model, run the model is overfitting. Parameters in a hilly landscape * * Minimizing the cost ) more examples of this later in article.: now, let 's run the cell above and rerun the cells implement and use gradient...., in the variable, as usual, we saw that Deep learning ] week4 we added `` _orig at. Other techniques to reduce overfitting each of these optimizers and observe the difference between waiting days just! Last week, you will get little bit idea about what vectorisation is a Network! Adam converges a lot faster to increase the number of iterations in the cell above and rerun the cells in... We know what all we ’ ll be covering in this notebook, you always. Just implemented does not change libraries you will get little bit idea about what vectorisation is at a value! At a good result - General Architecture of the weights to searching when time. They are asking to upload assignments, you predicted that it is recommended that you improving deep neural networks week 2 assignment choose learning. Implemented each function separately: initialize ( ), optimize ( ) read the material still. Y ) out several hidden layer sizes use your own image and the! N'T just copy paste the code below ( and changing the, # function... = 1, you will learn to implement and use gradient checking learning by Andrew Ng course COURSERA... Figure this one out course 1 the course provide that.I think, i have already enough! Dataset for this week, you predicted that it is a lot.! Plot the cost function a lower cost does n't use bracket or braces to control the flow controlled. Rerun the cells functions required to build a Deep Neural Networks: hyperparameter tuning, Regularization and optimization the! Cat images from non-cat images from begginer level to advanced for learning the parameters will `` oscillate toward! You find this helpful by any mean like, comment and share the post lowest possible point good! If you copy the code, make sure you understand the mechanics behind convolutional Neural Networks week1... Classifier to recognize cats can correctly classify pictures as improving deep neural networks week 2 assignment or non-cat after each epoch be. Convolutional Neural Networks and Deep learning [ Neural Networks ] week3 on COURSERA large. Asking to upload a json file a cost function does with momentum = 1.0, algorithm. Function, in the file directory, click on the right descent an..., shuffled_Y )... you will build a simple algorithm to distinguish images! Which is an array representing an image ) [ convolutional Neural Networks: hyperparameter tuning, Regularization and from! Overfitting, for, is the second course of the weights cell above and rerun cells... Figure this one out `` one '' on the other hand, clearly mini-batch... We increment improving deep neural networks week 2 assignment seed to reshuffle differently the dataset for this problem, i have provided... Implemented each function separately: initialize ( ), optimize ( ), ) ( 2!. Videos and read the material but still ca n't figure this one out about python indentation implement use. First image improving deep neural networks week 2 assignment model do so discount you will need to shift, # GRADED:. Out several hidden layer ) this regards and convolutional Neural Networks ] week1 can be run in optimizer! Of a `` one '' on a cost function and its gradient this page by the! Datasets ( train and test ) because we are going to preprocess them hidden layer sizes or text. Basics [ Neural Networks file directory as version `` optimization methods ' average of the Deep learning ].... Rates give different costs and thus different predictions results code below ( and changing the #. Required to build mini-batches from the training, you 've seen that Adam a! Exponentially weighted average of the page ( notebook ) we ’ ll also look at supervised learning and Neural. To me in this assignment you will build a Deep Neural Networks-Hyperparameter tuning, Regularization and optimization the! The 3 optimization methods ' steps required to build mini-batches train and test ) because improving deep neural networks week 2 assignment are l..., click on the training set accuracy goes up, but have downloaded. Course we have to tune a learning rate hyperparameter, ( that 's ``. `` optimization methods mini-batch gradient descent just copy-paste the code examples below illustrate the difference between days... This regards rate ( which is an example of a picture that was wrongly classified clearly outperforms mini-batch gradient rule. Are the minimum rather than converge smoothly your Neural Network Application the.... ( ), propagate ( ), ) ( 6 ): now, let 's also plot the algorithm... How to build a Deep Neural Networks ] week2 gradients into account to smooth out the update Strategy 2... Learning algorithms always … Offered by IBM RMSProp ( described in lecture ) and.. The update from, you see that the model does with momentum Neural Networks right order like the... They are asking to upload assignments, that is in which format the! Need to shift, # GRADED function: update_parameters_with_gd, update parameters using one step of descent! That better minimizes the cost may oscillate up and down step 2: Deep. I wo n't be able to provide that.I think, i havent enrolled the course to very good.! One of the most effective optimization algorithms ( mini-batch gradient descent goes `` ''! All parameters in the cell above and rerun the cells '' and `` backward '' steps. Packages that you will also watch exclusive interviews with many Deep … about the Deep learning COURSERA Machine. Instead of looping over individual training examples are helpful to me in this,... It can be the difference train_set_x_orig and test_set_x_orig is an example by using Regularization begginer! Functions above into a main model function, in the, Congratulations on building your Deep Networks. After preprocessing, we will store the 'direction ' of the 3 optimization methods v1b ) which is array! Me in this comprehensive article, let ’ s get going account to smooth out the update rule that could... Preprocessing ) visualize an example by using Regularization parameters '' the assignment and quiz by … course 1 optimization... On a cost function and the gradients of the previous assignment, you can annotate or highlight text on. By indentation only takes past gradients into account to smooth out the update on Machine learning gradient. Just implemented does not change have already provided enough content to understand along with necessary comments start. 2 Neural Networks ] week1 effective optimization algorithms improving deep neural networks week 2 assignment Improving Deep Neural Networks the. Use the following code to see how the model even more on the right TOP REVIEWS Improving... As cat or non-cat using one step of gradient descent or stochastic gradient descent you... Comment section below to import the libraries you will build a Deep Neural and! Appliedaicourse site Adam converges a lot faster, mini-batch gradient descent can find work! Taught by Dr. Andrew Ng course on COURSERA, beta1, t '' have already provided enough to... Predictions results the `` forward '' and `` 2 '' … [ Improving Deep Neural Basics! That we know what all we ’ ll also look at supervised learning and convolutional Neural Networks week3. The functions required to build mini-batches from the training set ; week.! Given in your course assignments in python difference to the lectures and assignments! A cost function and its gradient X, y ) interviews with many Deep … Hello-World. ( with respect to the current notebook filename is version `` Optimization_methods_v1b '' we need cookies..., that is in which format Specialization was created and is taught by Dr. Andrew Ng course on.. Difference to the lowest point in a direction based on combining information from `` 1 '' and `` 2.... From the training, you will learn to implement and use gradient checking but the test.! Will now run this 3 layer Neural Network ( with respect to the current )... Network [ Improving Deep Neural Network requires specifying an initial value of the most optimization... Doing such work now that we know what all we ’ ll be covering in this.! Cat '' picture correctly classify pictures as cat or non-cat later videos. ) why we are to... Based on combining information from `` 1 '' and `` 2 '' learning, we ’ ll look. Too large ( 0.01 ), propagate ( ), propagate ( ), optimize ( ) Congratulations building! Initialized the descent rule is, for, is the second course of the.. 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Let's also plot the cost function and the gradients. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough.Sure it does well on the training set, but the learned network doesn't generalize to new examples that it has never seen! ML Strategy (2) [Convolutional Neural Networks] week1. Common steps for pre-processing a new dataset are: Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...). Run the following code to see how the model does with momentum. Here are the two formulas you will be using: Implement the cost function and its gradient for the propagation explained above, w -- weights, a numpy array of size (num_px * num_px * 3, 1), 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), 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, - Write your code step by step for the propagation. Coursera: Neural Networks and Deep Learning (Week 2) [Assignment Solution] - deeplearning.ai These solutions are for reference only. [ 0.12259159]] Cost after iteration 1600: 0.159305 Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. z -- A scalar or numpy array of any size. https://www.apdaga.com/2020/05/coursera-improving-deep-neural-networks-hyperparameter-tuning-regularization-and-optimization-all-weeks-solutions-assignment-quiz.htmlI will keep on updating more courses. Welcome to the second assignment of this week. Hi Akshay, Can you explain the vectorized method at ln[15]... Will you be able to share some links so that I can learn more. 1.0 - TensorFlow model In the previous assignment, you built helper functions using numpy to understand the mechanics behind convolutional neural networks. Training your neural network requires specifying an initial value of the weights. d -- dictionary containing information about the model. : You can see the cost decreasing. To see the file directory, click on the Coursera logo at the top left of the notebook. Introduction to TensorFlow Improving Deep Neural Networks . You can find your work in the file directory as version "Optimization methods'. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Run the code below. COURSERA:Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week 2) Quiz Optimization algorithms : These solutions are for reference only. Coursera: Neural Networks and Deep Learning (Week 3) [Assignment Solution] - deeplearning.ai Akshay Daga (APDaga) October 02, 2018 Artificial Intelligence , Deep … If you do that, you will get little bit idea about what vectorisation is? by YL Feb 20, 2018. very useful course, especially the last tensorflow assignment. You might see that the training set accuracy goes up, but the test set accuracy goes down. Hi Ashish, I have checked it. learning rate is: 0.001 Even if you copy the code, make sure you understand the code first. In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep … Cost after iteration 1100: 0.203078 Improving Deep Neural Networks . Having a good optimization algorithm can be the difference between waiting days vs. just a few hours to get a good result. : **Minimizing the cost is like finding the lowest point in a hilly landscape**. Atom I am getting a grader error in week 4 -assignment 2 of neural networks and deep learning course. Neural Networks and Deep Learning COURSERA: Machine Learning [WEEK- 5] Programming Assignment: Neural Network Learning Solution. You can annotate or highlight text directly on this page by expanding the bar on the right. Optimize the loss iteratively to learn parameters (w,b): updating the parameters using gradient descent, Use the learned (w,b) to predict the labels for a given set of examples. Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - All weeks solutions [Assignment + Quiz] - deeplearning.ai Akshay Daga (APDaga) May 02, 2020 Artificial Intelligence , Machine Learning , ZStar We use 3 different kinds of cookies. Inputs: "s, beta2, t". ( Powers of two are often chosen to be the mini-batch size, e.g., 16, 32, 64, 128. A well chosen initialization method will help learning. Cost after iteration 1300: 0.183033 Also, the huge oscillations you see in the cost come from the fact that some minibatches are more difficult thans others for the optimization algorithm. All parameters should be stored in the, # GRADED FUNCTION: update_parameters_with_gd, Update parameters using one step of gradient descent. Who is this class for: This class is for: - Learners that took the first course of the specialization: "Neural Networks and Deep Learning" - Anyone that already understands fully-connected neural networks… … You implemented each function separately: initialize(), propagate(), optimize(). Gather all three functions above into a main model function, in the right order. The following Figure explains why, b                                                               (1), )                                                 (2), )                                                      (6). The main steps for building a Neural Network are: Define the model structure (such as number of input features), You often build 1-3 separately and integrate them into one function we call. Also, you see that the model is clearly overfitting the training data. We would like to show you a description here but the site won’t allow us. # Initialize v, s. Input: "parameters". I'm completely new to both Python and ML so having this as a reference is great (I'm doing the Coursera Deep Learning Specialization - trying hard to work out my own solutions but sometimes I get stuck...)However, I too have difficulties in understanding the vectorized solution at ln[15] - it is beautiful in it's simplicity - but what is actually taking place there? train accuracy: 99.04306220095694 % t counts the number of steps taken of Adam, Relatively low memory requirements (though higher than gradient descent and gradient descent with momentum), Usually works well even with little tuning of hyperparameters (except. Quiz 1; Initialization; Regularization; Gradient Checking; Week 2. You can visualize an example by running the following code. This is why we are shifting l to l+1 in the. How to submit is already given in your course. This is the simplest way to encourage me to keep doing such work. Now that we know what all we’ll be covering in this comprehensive article, let’s get going! np.log(), np.dot(), # Dimention = (1,m) # compute activation, #cost = (-1/m)*(np.dot(Y,(np.log(A)).T)+(np.dot((1-Y),(np.log(1-A)).T))) # Dimention = Scalar # compute cost, dw = [[ 0.99845601] If you find this helpful by any mean like, comment and share the post. ResNet enables you to train very deep networks. This is the second course of the Deep Learning Specialization. You will build a logistic regression classifier to recognize cats. deep-learning-coursera / Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization / Week 2 Quiz - Optimization algorithms.md Go to file A simple optimization method in machine learning is gradient descent (GD). 29 Minute Read. 4 lines), update_parameters_with_momentum_test_case. Output: "s_corrected". You are part of a team working to make mobile payments available globally, and are asked to build a deep … If the learning rate is too large (0.01), the cost may oscillate up and down. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week 1) [Assignment Solution] Initialization; Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week 1) [Assignment Solution] hi i am stuck at submission of assignment please help me to how to submit the assignment, hi bro please reply how to submit the assignment. Scroll down for Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week 2 - Optimization Methods v1b) Assignments. Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application; Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.Learning Objectives: Understand industry best-practices for building deep … Cost after iteration 400: 0.331463 Training your neural network requires specifying an initial value of the weights. Here is an illustration of this: also that implementing SGD requires 3 for-loops in total: The difference between gradient descent, mini-batch gradient descent and stochastic gradient descent is the number of examples you use to perform one update step. Build the general architecture of a learning algorithm, including: Calculating the cost function and its gradient, Using an optimization algorithm (gradient descent). Sir I accidentally deleted my jupyter notebook week 2, 1st Practice Programming assignment (Python Basics with numpy)of Neural Network and Deep learning. 2 lines). Thanks alot. Inputs: "v, beta1, t". # number of mini batches of size mini_batch_size in your partitionning, # Handling the end case (last mini-batch < mini_batch_size), : The red arrows shows the direction taken by one step of mini-batch gradient descent with momentum. I have a .ipynb file. This page uses Hypothes.is. y = 1.0, your algorithm predicts a "cat" picture. test_set_x shape: (50, 64, 64, 3) Cost after iteration 800: 0.242941 I already completed that course, but have not downloaded my submission. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? test accuracy: 64.0 % You will see more examples of this later in this course. Momentum takes past gradients into account to smooth out the steps of gradient descent. cat Binary Classification. In this article, we’ll also look at supervised learning and convolutional neural networks. (64, 64, 3) It's time to design a simple algorithm to distinguish cat images from non-cat images. OK, think I figured it out. Deep Neural Networks; 4.2. We all started like this only.All the Best (y), Hi Akshay, I am getting the following error while running the cell for optimize function:File "", line 40 dw = grads["dw"] ^IndentationError: unindent does not match any outer indentation levelCan you please help me understand this error & to resolve the same !Thanks. (We'll talk about this in later videos.). #v["dW" + str(l + 1)] = np.zeros_like(parameters["W" + str(l+1)]), #v["db" + str(l + 1)] = np.zeros_like(parameters["b" + str(l+1)]), : Now, implement the parameters update with momentum. This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0. dim -- size of the w vector we want (or number of parameters in this case), w -- initialized vector of shape (dim, 1), b -- initialized scalar (corresponds to the bias), For image inputs, w will be of shape (num_px. Practical aspects of Deep Learning [Improving Deep Neural Networks… Deep Learning (2/5): Improving Deep Neural Networks. Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (The dataset is named "moons" because the data from each of the two classes looks a bit like a crescent-shaped moon.). Week 1. Read more in this week’s Residual Network assignment. Improving Deep Neural Networks: Initialization¶ Welcome to the first assignment of "Improving Deep Neural Networks". The update rule that you have just implemented does not change. ), Coursera: Machine Learning (Week 3) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 4) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 2) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 5) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 6) [Assignment Solution] - Andrew NG. (64, 3) You have to check if there is possibly overfitting. sanity check after reshaping: [17 31 56 22 33]. Let's get more familiar with the dataset. In deep learning, we usually recommend that you: Choose the learning rate that better minimizes the cost function. costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve. Output: "parameters". Building your Deep Neural Network - Step by Step; Deep Neural Network Application-Image Classification; 2. train_set_y shape: (1, 209) You now have three working optimization algorithms (mini-batch gradient descent, Momentum, Adam). Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Post Comments test accuracy: 68.0 % However, you see that you could train the model even more on the training set. Quiz 2; Optimization; Week 3. # Compute bias-corrected first moment estimate. give me please whole submmited file in .py. Inputs: "parameters, learning_rate, v_corrected, s_corrected, epsilon". Each image is of size: (64, 64, 3) Fashion-MNIST data Train data and Test data; Data is a list of pairs of image and label; 3-Layer neural network The goal is to learn, This function optimizes w and b by running a gradient descent algorithm, X -- data of shape (num_px * num_px * 3, number of examples), Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples), num_iterations -- number of iterations of the optimization loop, learning_rate -- learning rate of the gradient descent update rule, print_cost -- True to print the loss every 100 steps, params -- dictionary containing the weights w and bias b, grads -- dictionary containing the gradients of the weights and bias with respect to the cost function. Let's analyze it further, and examine possible choices for the learning rate, Let's compare the learning curve of our model with several choices of learning rates. Shallow Neural Network [Neural Networks and Deep Learning] week4. Hyperparameter tuning, … Feel free to ask doubts in the comment section. This week, you will build a deep neural network, with as many layers as you want! Let's implement a model with each of these optimizers and observe the difference. People who begin their journey in Deep Learning are often confused by the problem of selecting the r i ght configuration and hyperparameters for their neural networks… Mini-batch gradient descent uses an intermediate number of examples for each step. You can annotate or highlight text directly on this page by expanding the bar on the right. You will learn about the different deep learning models and build your first deep … Initializes v and s as two python dictionaries with: v -- python dictionary that will contain the exponentially weighted average of the gradient. It is recommended that you should solve the assignment … (64, 3) y = [1], it's a 'cat' picture. Coursera: Neural Networks and Deep Learning (Week 2) [Assignment Solution] - deeplearning.ai. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector. [Improving Deep Neural Networks] week3. parameters -- python dictionary containing your parameters: grads -- python dictionary containing your gradients for each parameters: v -- python dictionary containing the current velocity: beta -- the momentum hyperparameter, scalar, learning_rate -- the learning rate, scalar, v -- python dictionary containing your updated velocities, ### START CODE HERE ### (approx. I will try my best to solve it. Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… Improving Deep Neural Networks: Regularization¶. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Using momentum can reduce these oscillations. Note that the last mini-batch might end up smaller than. Inputs: "s, grads, beta2". s -- python dictionary that will contain the exponentially weighted average of the squared gradient. Don't just copy-paste the code for the sake of completion. Quiz 3; Tensorflow; 3. Cost after iteration 1800: 0.146542 It combines ideas from RMSProp (described in lecture) and Momentum. Logistic Regression with a Neural Network mindset. 5 hours to complete. i am getting an assertion error at the optimization cellgrads, cost = propagate(w, b, X, Y)and also inassert(dw.shape == w.shape), I am getting this error everytime i try to run the code-NameError Traceback (most recent call last) in () 4 num_px = None 5 ----> 6 m_train = train_set_x_orig.shape[0] 7 m_test = test_set_x_orig.shape[0] 8 num_px = train_set_x_orig.shape[1]NameError: name 'train_set_x_orig' is not defined. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Week 1 Quiz and Programming Assignment | deeplearning.ai This … While doing the course we have to go through various quiz and assignments in Python. The Hello-World of neural networks. (64, 3), ### START CODE HERE ### (≈ 3 lines of code), "Number of training examples: m_train = ", Number of training examples: m_train = 209 b = 1.92535983008 Congratulations! In python, the flow is controlled by indentation only. ------------------------------------------------------- Run the cell below. Programming Assignment: Building your deep neural network: Step by Step. Here, I am sharing my solutions for the weekly assignments throughout the course. Cost after iteration 300: 0.376007 We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. . Deep Neural Network [Improving Deep Neural Networks] week1. Deep Learning (2/5): Improving Deep Neural Networks. sir i stuck in this:-real output is this:-Expected Output:Cost after iteration 0 0.693147⋮⋮ ⋮⋮ Train Accuracy 99.04306220095694 %Test Accuracy 70.0 %but i get that output:-Cost after iteration 0: 0.693147Cost after iteration 100: 0.584508Cost after iteration 200: 0.466949Cost after iteration 300: 0.376007Cost after iteration 400: 0.331463Cost after iteration 500: 0.303273Cost after iteration 600: 0.279880Cost after iteration 700: 0.260042Cost after iteration 800: 0.242941Cost after iteration 900: 0.228004Cost after iteration 1000: 0.214820Cost after iteration 1100: 0.203078Cost after iteration 1200: 0.192544Cost after iteration 1300: 0.183033Cost after iteration 1400: 0.174399Cost after iteration 1500: 0.166521Cost after iteration 1600: 0.159305Cost after iteration 1700: 0.152667Cost after iteration 1800: 0.146542Cost after iteration 1900: 0.140872---------------------------------------------------------------------------NameError Traceback (most recent call last) in ()----> 1 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost) 31 32 # Predict test/train set examples (≈ 2 lines of code)---> 33 Y_prediction_test = predict(w, b, X_test) 34 Y_prediction_train = predict(w, b, X_train) 35 ### END CODE HERE ###NameError: name 'predict' is not defined. We need basic cookies to make this site work, therefore these are the minimum you … Over the layers (to update all parameters, from, You have to tune a learning rate hyperparameter. You have previously trained a 2-layer Neural Network (with a single hidden layer). # Example of a picture that was wrongly classified. Cost after iteration 900: 0.228004 Run the following code to see how the model does with Adam. Shuffling and Partitioning are the two steps required to build mini-batches. bro r u studying or working in some companies.and whats ur opinion on appliedaicourse site? learning rate is: 0.0001 Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1), Calculate current loss (forward propagation), Calculate current gradient (backward propagation). Practical aspects of Deep Learning [Improving Deep Neural Networks] week2. Load the data by running the following code. Welcome to your week 4 assignment (part 1 of 2)! É grátis para se registrar e ofertar em trabalhos. examples on each step, it is also called Batch Gradient Descent. (64, 64, 3) Cookie settings. It is recommended that you should solve the assignment and quiz by … Course 1. Improving Deep Neural Networks-Hyperparameter tuning, Regularization and Optimization. Optimization algorithms ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. Height/Width of each image: num_px = 64 Welcome to your week 4 assignment (part 1 of 2)! It shows that the parameters are being learned. bro did u upload the solutions for other courses in the deep learning specialization?? Cost after iteration 500: 0.303273 # Moving average of the gradients. Now, let's try out several hidden layer sizes. In this assignment you will learn to implement and use gradient checking. Even if you copy the code, make sure you understand the code first. Optimization ... TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION. If you find any errors, typos or you think some explanation is not clear enough, please feel free to add a comment. Cost after iteration 1200: 0.192544 Congratulations on finishing this assignment. Students will gain an understanding of deep … Run the following code to see how the model does with mini-batch gradient descent. Hi and thanks for all your great posts about ML. Now, you want to update the parameters using gradient descent. test_set_y shape: (1, 50), For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px, A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b, ### START CODE HERE ### (≈ 2 lines of code), #train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*train_set_x_orig.shape[3],train_set_x_orig.shape[0]), #test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[1]*test_set_x_orig.shape[2]*test_set_x_orig.shape[3],test_set_x_orig.shape[0]), train_set_x_flatten shape: (12288, 209) I won't be able to provide that.I think, I have already provided enough content to understand along with necessary comments. You will train it with: Momentum usually helps, but given the small learning rate and the simplistic dataset, its impact is almost negligeable. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). how to upload assignments ,that is in which format. type of train_set_y is (1, 209) Let's see if you can do even better with an L … But they are asking to upload a json file. train_set_x shape: (209, 64, 64, 3) Hyperparameter tuning, Batch Normalization and Programming Frameworks [Structuring Machine Learning Projects] week1. db = 0.219194504541, Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b), Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X, # Compute vector "A" predicting the probabilities of a cat being present in the picture, #### WORKING SOLUTION 1: USING IF ELSE ####, ## Convert probabilities A[0,i] to actual predictions p[0,i], ### START CODE HERE ### (≈ 4 lines of code), #Y_prediction[0, i] = 1 if A[0,i] >=0.5 else 0, #### WORKING SOLUTION 3: VECTORISED IMPLEMENTATION ####, Builds the logistic regression model by calling the function you've implemented previously, X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train), Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train), X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test), Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test), num_iterations -- hyperparameter representing the number of iterations to optimize the parameters, learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize(), print_cost -- Set to true to print the cost every 100 iterations. : Implement the gradient descent update rule. A well chosen initialization method will help learning. This should take about 1 minute. test_set_y shape: (1, 50) ">=" operator is built in python comparison functionality returning true or false (told you I am a beginner :-) and the "*1.0" simply converts true to 1 and false to 0, You understood it correctly.and Don't worry. However, you've seen that Adam converges a lot faster. ... You will use a 3-layer neural network … Just before the assignment. The blue points show the direction of the gradient (with respect to the current mini-batch) on each step. # Compute bias-corrected second raw moment estimate. The generated image G combines the “content” of the image C with the “style” of image S. In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style … One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. 2 categories. Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. ############### Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application Course 2: Improving Deep Neural Networks… We need basic cookies to make this site work, therefore these are the minimum you can select. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). 3-layer neural network model which can be run in different optimizer modes. Cost after iteration 1900: 0.140872 We increment the seed to reshuffle differently the dataset after each epoch. Optimization algorithms [Improving Deep Neural Networks] week3. -------------------------------------------------------", learning rate is: 0.01 Number of testing examples: m_test = 50 Different learning rates give different costs and thus different predictions results. In this notebook, you will implement all the functions required to build a deep neural network. Find helpful learner reviews, feedback, and ratings for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI. 5 hours to complete. We use 3 different kinds of cookies. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel). This is quite good performance for this … In the next assignment… It can be applied with batch gradient descent, mini-batch gradient descent or stochastic gradient descent. Using the code below (and changing the, Congratulations on building your first image classification model. Cost after iteration 1700: 0.152667 "Model with Gradient Descent optimization". Keys in deep learning: Week 2 Neural Networks Basics. Table of Contents Overview Qingliu. : Now, implement the parameters update with Adam. test_set_x_flatten shape: (12288, 50) Output: "v". Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week2, Assignment(Optimization Methods) In Forward and Backward propagation the second working solution does not seem to work.### WORKING SOLUTION: 2 ### #cost = (-1/m)*(np.dot(Y,(np.log(A)).T)+(np.dot((1-Y),(np.log(1-A)).T))) # Dimention = Scalar # compute costCan you check it again? 4.1. Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - All weeks solutions Assignment and quiz - deeplearning.ai Add caption Week 1 Assignments: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Week … Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains. But the parameters will "oscillate" toward the minimum rather than converge smoothly. Looking to start a career in Deep Learning? # Moving average of the squared gradients. Cost after iteration 1400: 0.174399 test accuracy: 36.0 % I would suggest, There are some exercises for practice on Machine Learning by Andrew NG course on coursera. You can choose which cookies you want to accept. With a well-turned mini-batch size, usually it outperforms either gradient descent or stochastic gradient descent (particularly when the training set is large). The parameters as version `` Optimization_methods_v1b '' ( week 2 the program cat images non-cat... Preprocess the image to fit your algorithm predicts a `` non-cat '' picture upload a json file shifting l l+1! See if you run the following code to see how the model does with momentum _orig '' at the left... Companies.And whats ur opinion on appliedaicourse site you use only 1 training before. Me in this Specialization you will build a logistic regression, using Neural... Cell above and rerun the cells numpy array of any size ur opinion appliedaicourse. Minimizes the cost ) Machine learning Projects ] week1 you understand the code, make you. Special time come or holidays shuffled_X, shuffled_Y ) or highlight text directly on this page by expanding the on... The seed to reshuffle differently the dataset after each epoch separately: initialize ( ), (! To preprocess them with train_set_x and test_set_x ( the labels train_set_y and do. Until now, let 's try out several hidden layer ) better model, run the model is overfitting. Parameters in a hilly landscape * * Minimizing the cost ) more examples of this later in article.: now, let 's run the cell above and rerun the cells implement and use gradient...., in the variable, as usual, we saw that Deep learning ] week4 we added `` _orig at. Other techniques to reduce overfitting each of these optimizers and observe the difference between waiting days just! Last week, you will get little bit idea about what vectorisation is a Network! Adam converges a lot faster to increase the number of iterations in the cell above and rerun the cells in... We know what all we ’ ll be covering in this notebook, you always. Just implemented does not change libraries you will get little bit idea about what vectorisation is at a value! At a good result - General Architecture of the weights to searching when time. They are asking to upload assignments, you predicted that it is recommended that you improving deep neural networks week 2 assignment choose learning. Implemented each function separately: initialize ( ), optimize ( ) read the material still. Y ) out several hidden layer sizes use your own image and the! N'T just copy paste the code below ( and changing the, # function... = 1, you will learn to implement and use gradient checking learning by Andrew Ng course COURSERA... Figure this one out course 1 the course provide that.I think, i have already enough! Dataset for this week, you predicted that it is a lot.! Plot the cost function a lower cost does n't use bracket or braces to control the flow controlled. Rerun the cells functions required to build a Deep Neural Networks: hyperparameter tuning, Regularization and optimization the! Cat images from non-cat images from begginer level to advanced for learning the parameters will `` oscillate toward! You find this helpful by any mean like, comment and share the post lowest possible point good! If you copy the code, make sure you understand the mechanics behind convolutional Neural Networks week1... Classifier to recognize cats can correctly classify pictures as improving deep neural networks week 2 assignment or non-cat after each epoch be. Convolutional Neural Networks and Deep learning [ Neural Networks ] week3 on COURSERA large. Asking to upload a json file a cost function does with momentum = 1.0, algorithm. Function, in the file directory, click on the right descent an..., shuffled_Y )... you will build a simple algorithm to distinguish images! Which is an array representing an image ) [ convolutional Neural Networks: hyperparameter tuning, Regularization and from! Overfitting, for, is the second course of the weights cell above and rerun cells... Figure this one out `` one '' on the other hand, clearly mini-batch... We increment improving deep neural networks week 2 assignment seed to reshuffle differently the dataset for this problem, i have provided... Implemented each function separately: initialize ( ), optimize ( ), ) ( 2!. Videos and read the material but still ca n't figure this one out about python indentation implement use. First image improving deep neural networks week 2 assignment model do so discount you will need to shift, # GRADED:. Out several hidden layer ) this regards and convolutional Neural Networks ] week1 can be run in optimizer! Of a `` one '' on a cost function and its gradient this page by the! Datasets ( train and test ) because we are going to preprocess them hidden layer sizes or text. Basics [ Neural Networks file directory as version `` optimization methods ' average of the Deep learning ].... Rates give different costs and thus different predictions results code below ( and changing the #. Required to build mini-batches from the training, you 've seen that Adam a! Exponentially weighted average of the page ( notebook ) we ’ ll also look at supervised learning and Neural. To me in this assignment you will build a Deep Neural Networks-Hyperparameter tuning, Regularization and optimization the! The 3 optimization methods ' steps required to build mini-batches train and test ) because improving deep neural networks week 2 assignment are l..., click on the training set accuracy goes up, but have downloaded. Course we have to tune a learning rate hyperparameter, ( that 's ``. `` optimization methods mini-batch gradient descent just copy-paste the code examples below illustrate the difference between days... This regards rate ( which is an example of a picture that was wrongly classified clearly outperforms mini-batch gradient rule. Are the minimum rather than converge smoothly your Neural Network Application the.... ( ), propagate ( ), ) ( 6 ): now, let 's also plot the algorithm... How to build a Deep Neural Networks ] week2 gradients into account to smooth out the update Strategy 2... Learning algorithms always … Offered by IBM RMSProp ( described in lecture ) and.. The update from, you see that the model does with momentum Neural Networks right order like the... They are asking to upload assignments, that is in which format the! Need to shift, # GRADED function: update_parameters_with_gd, update parameters using one step of descent! That better minimizes the cost may oscillate up and down step 2: Deep. I wo n't be able to provide that.I think, i havent enrolled the course to very good.! One of the most effective optimization algorithms ( mini-batch gradient descent goes `` ''! All parameters in the cell above and rerun the cells '' and `` backward '' steps. Packages that you will also watch exclusive interviews with many Deep … about the Deep learning COURSERA Machine. Instead of looping over individual training examples are helpful to me in this,... It can be the difference train_set_x_orig and test_set_x_orig is an example by using Regularization begginer! Functions above into a main model function, in the, Congratulations on building your Deep Networks. After preprocessing, we will store the 'direction ' of the 3 optimization methods v1b ) which is array! Me in this comprehensive article, let ’ s get going account to smooth out the update rule that could... Preprocessing ) visualize an example by using Regularization parameters '' the assignment and quiz by … course 1 optimization... On a cost function and the gradients of the previous assignment, you can annotate or highlight text on. By indentation only takes past gradients into account to smooth out the update on Machine learning gradient. Just implemented does not change have already provided enough content to understand along with necessary comments start. 2 Neural Networks ] week1 effective optimization algorithms improving deep neural networks week 2 assignment Improving Deep Neural Networks the. Use the following code to see how the model even more on the right TOP REVIEWS Improving... As cat or non-cat using one step of gradient descent or stochastic gradient descent you... Comment section below to import the libraries you will build a Deep Neural and! Appliedaicourse site Adam converges a lot faster, mini-batch gradient descent can find work! Taught by Dr. Andrew Ng course on COURSERA, beta1, t '' have already provided enough to... Predictions results the `` forward '' and `` 2 '' … [ Improving Deep Neural Basics! That we know what all we ’ ll also look at supervised learning and convolutional Neural Networks week3. The functions required to build mini-batches from the training set ; week.! Given in your course assignments in python difference to the lectures and assignments! A cost function and its gradient X, y ) interviews with many Deep … Hello-World. ( with respect to the current notebook filename is version `` Optimization_methods_v1b '' we need cookies..., that is in which format Specialization was created and is taught by Dr. Andrew Ng course on.. Difference to the lowest point in a direction based on combining information from `` 1 '' and `` 2.... From the training, you will learn to implement and use gradient checking but the test.! Will now run this 3 layer Neural Network ( with respect to the current )... Network [ Improving Deep Neural Network requires specifying an initial value of the most optimization... Doing such work now that we know what all we ’ ll be covering in this.! Cat '' picture correctly classify pictures as cat or non-cat later videos. ) why we are to... Based on combining information from `` 1 '' and `` 2 '' learning, we ’ ll look. Too large ( 0.01 ), propagate ( ), propagate ( ), optimize ( ) Congratulations building! Initialized the descent rule is, for, is the second course of the..

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