Construct net

Southern Mississippi, Hattiesburg, Mississippi. National Center for O*NET Development. Interest Profiler (IP) Short Form. O*NET Resource Center. Retrieved from https: //www. onetcenter. org/IPSF. htlm Prevatt, F. , Li, H. , Welles, T. , Festa-Dreher, D. , Yelland, S. , & Lee, J. (2011). The Academic Success Inventory for College Students: Scale Development and Practical Implications for Use with Students. Journal Of College Admission, (211), 26 -31. Swanson, J. L. , & Hansen, J. C. (1986). A clarification of Holland's construct of differentiation: The importance of score elevation. Journal Of Vocational Behavior, 28(2), 163 -173. doi: 10. 1016/0001 -8791(86)90049 -7 Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321 – 327. Welles, T. L. (2010). An analysis of the Academic Success Inventory for College Students: construct validity and factor scale invariance. Unpublished doctoral dissertation, Florida State University, Tallahassee, FL. Wooten, E. & Bullock-Yowell, E. (2015). Does overall level of career interest relate to student academic success. Poster presentation at the USM Undergraduate Research Symposium, University of Southern Mississippi, Hattiesburg, MS.

Then prepare the Sequence class in the utils module of Keras to compute, load, and vectorize batches of data. We will then construct the initialization function, an additional function to compute the length and the final function that will generate batches of data.from tensorflow import kerasimport numpy as npfrom tensorflow.keras.preprocessing.image import load_imgclass OxfordPets(keras.utils.Sequence): """Helper to iterate over the data (as Numpy arrays).""" def __init__(self, batch_size, img_size, input_img_paths, target_img_paths): self.batch_size = batch_size self.img_size = img_size self.input_img_paths = input_img_paths self.target_img_paths = target_img_paths def __len__(self): return len(self.target_img_paths) // self.batch_size def __getitem__(self, idx): """Returns tuple (input, target) correspond to batch #idx.""" i = idx * self.batch_size batch_input_img_paths = self.input_img_paths[i : i + self.batch_size] batch_target_img_paths = self.target_img_paths[i : i + self.batch_size] x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype="float32") for j, path in enumerate(batch_input_img_paths): img = load_img(path, target_size=self.img_size) x[j] = img y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype="uint8") for j, path in enumerate(batch_target_img_paths): img = load_img(path, target_size=self.img_size, color_mode="grayscale") y[j] = np.expand_dims(img, 2) # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2: y[j] -= 1 return x, yIn the next step, we will define the split between the training and the validation data, respectively. We do this step to ensure that there is no corruption between the integrity of the elements in the train and test sets accordingly. Both of these data entities must be viewed separately so that the model does not get a peek at the testing data. In the validation set, we will also perform an optional shuffle operation that will mix up all the images in the dataset, and we can obtain random samples for both the train and the validation images. We will then call the training values and the validation values individually and store them in their respective variables.import random# Split our img paths into a training and a validation setval_samples = 1000random.Random(1337).shuffle(input_img_paths)random.Random(1337).shuffle(target_img_paths)train_input_img_paths = input_img_paths[:-val_samples]train_target_img_paths = target_img_paths[:-val_samples]val_input_img_paths = input_img_paths[-val_samples:]val_target_img_paths = target_img_paths[-val_samples:]# Instantiate data Sequences for each splittrain_gen = OxfordPets( batch_size, img_size, train_input_img_paths, train_target_img_paths)val_gen = OxfordPets(batch_size, img_size, val_input_img_paths, val_target_img_paths)Once we have completed the following steps, we can proceed to construct our U-Net architecture.U-Net ModelThe U-Net model we will construct in this section is the exact same architecture as the one defined in the previous sections except for a few small modifications that we will discuss shortly. After the preparation of the dataset, we can construct our model accordingly. The model inculcates the image

Is your answer consistent with the net work done on the ball in motions I and 2? Explain. 2. How does the final speed of the ball in motion I compare to the final speed in motion 2? Explain. E. For motion 1, draw vectors in region II of the enlargement that represent the momentum of the ball at the top of the ramp and at the bottom of the ramp (i.e., at the top and bottom of region II). Use these vectors to construct the change in momentum vector llp. How is the direction of llp related to the direction of the net force on the ball as it rolls down the ramp? Is your answer consistent with the impulse-momentum theorem? Tutorials in Introductory Physics McDennon, Shaffer, & P.E.G., U. Wash. ©Prentice Hall, Inc. First Edition, 2002 Changes ;,, energy and momentum Mech 47 F. For motion 2, draw vectors in region II of the enlargement that represent the initial and the final momentum of the ball. Draw these vectors using the same scale that you used for motion I (i.e., the relative lengths should represent the relative magnitudes). Use these vectors to construct the change in momentum vector tJ.P for motion 2. 11p How should the direction of compare to the direction of the net force on the ball as it rolls down the ramp? If necessary, modify your diagram to be consistent with the impulse-momentum theorem. G. Consider the change in momentum vectors you constructed for motions

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