Using the life cycle image above to help you define meiosis- haplece a.pdf

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Using the life cycle image above to help you define meiosis, haplece and fertilization in this figure of Altermation of Generations in plants. Using the life cycle image above to help you define meiosis, haploid and fertilization in this figure of Alternation of Generations in nlante Describe endosymbiosis and secondary endosymbiosis in your own words. In angiosperms, pollen grains are released from the .... land on the stichy... and develop a tube down the length of the style to the ovules. anthers ... stigma carpels ... receptacles filaments ... ovary anthers ... sepal Question 38 (4 points) Match each statement with a group of plants. Lycophytes 1. the simplest plants 2. common vascular plant that Bryophytes produces haplaid speres.

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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.

Using the code below- I need help with creating code for the following.pdf

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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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