Using the following national income accounting data- compute (a) GDP-.pdf

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Using the following national income accounting data. compute (a) GDP. (b) NDP, and (c) Ni. All figures are in bilions. Instructions: Round your answers to 1 decimal place. a. G D P = $ bilion b. NDP = $ billion C. N I = $ bilion.

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Using the code below, I need help with creating code for the following: 1) Write Python code to plot the images from the first epoch. Take a screenshot of the images from the first epoch. 2) Write Python code to plot the images from the last epoch. Take a screenshot of the images from the last epoch. #Step 1: Import the required Python libraries: import numpy as np import matplotlib.pyplot as plt import keras from keras.layers import Input, Dense, Reshape, Flatten, Dropout from keras.layers import BatchNormalization, Activation, ZeroPadding2D from keras.layers import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam,SGD from keras.datasets import cifar10 #Step 2: Load the data. #Loading the CIFAR10 data (X, y), (_, _) = keras.datasets.cifar10.load_data() #Selecting a single class of images #The number was randomly chosen and any number #between 1 and 10 can be chosen X = X[y.flatten() == 8] #Step 3: Define parameters to be used in later processes. #Defining the Input shape image_shape = (32, 32, 3) latent_dimensions = 100 #Step 4: Define a utility function to build the generator. def build_generator(): model = Sequential() #Building the input layer model.add(Dense(128 * 8 * 8, activation="relu", input_dim=latent_dimensions)) model.add(Reshape((8, 8, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.78)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.78)) model.add(Activation("relu")) model.add(Conv2D(3, kernel_size=3, padding="same")) model.add(Activation("tanh")) #Generating the output image noise = Input(shape=(latent_dimensions,)) image = model(noise) return Model(noise, image) #Step 5: Define a utility function to build the discriminator. def build_discriminator(): #Building the convolutional layers #to classify whether an image is real or fake model = Sequential() model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=image_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.82)) model.add(LeakyReLU(alpha=0.25)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.82)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.25)) model.add(Dropout(0.25)) #Building the output layer model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) image = Input(shape=image_shape) validity = model(image) return Model(image, validity) #Step 6: Define a utility function to display th.

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Using the code below, I need help with the following 3 things: 1) Write Python code to plot the images from the first epoch. Take a screenshot of the images from the first epoch. 2) Write Python code to plot the images from the last epoch. Take a screenshot of the images from the last epoch. 3) Comment on the network performance. #Step 1: Import the required Python libraries: import numpy as np import matplotlib.pyplot as plt import keras from keras.layers import Input, Dense, Reshape, Flatten, Dropout from keras.layers import BatchNormalization, Activation, ZeroPadding2D from keras.layers import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam,SGD from keras.datasets import cifar10 #Step 2: Load the data. #Loading the CIFAR10 data (X, y), (_, _) = keras.datasets.cifar10.load_data() #Selecting a single class of images #The number was randomly chosen and any number #between 1 and 10 can be chosen X = X[y.flatten() == 8] #Step 3: Define parameters to be used in later processes. #Defining the Input shape image_shape = (32, 32, 3) latent_dimensions = 100 #Step 4: Define a utility function to build the generator. def build_generator(): model = Sequential() #Building the input layer model.add(Dense(128 * 8 * 8, activation="relu", input_dim=latent_dimensions)) model.add(Reshape((8, 8, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.78)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.78)) model.add(Activation("relu")) model.add(Conv2D(3, kernel_size=3, padding="same")) model.add(Activation("tanh")) #Generating the output image noise = Input(shape=(latent_dimensions,)) image = model(noise) return Model(noise, image) #Step 5: Define a utility function to build the discriminator. def build_discriminator(): #Building the convolutional layers #to classify whether an image is real or fake model = Sequential() model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=image_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.82)) model.add(LeakyReLU(alpha=0.25)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.82)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.25)) model.add(Dropout(0.25)) #Building the output layer model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) image = Input(shape=image_shape) validity = model(image) return Model(image, validity) #Step 6: Define a .

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