Without running the code in Python, describe the architecture for the following deep learning model, including number of layers, dimension of images in each layer, number of parameters in each layer. Show details of all you calculations. inputs = keras.Input(shape=(36, 36, 1)) x = layers.Conv2D(filters=16, kernel_size=5, activation="relu")(inputs) x = layers.MaxPooling2D(pool_size=3)(x) x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x) x = layers.MaxPooling2D(pool_size=2)(x) x = layers.Conv2D(filters=128, kernel_size=2, activation="relu")(x) x = layers.Flatten()(x) x = layers.Dense(128, activation="relu")(x) x = layers.Dropout(0.5)(x) outputs = layers.Dense(10, activation="softmax")(x) model = keras.Model(inputs=inputs, outputs=outputs)
Without running the code in Python, describe the architecture for the following deep learning model, including number of layers, dimension of images in each layer, number of parameters in each layer. Show details of all you calculations. inputs = keras.Input(shape=(36, 36, 1)) x = layers.Conv2D(filters=16, kernel_size=5, activation="relu")(inputs) x = layers.MaxPooling2D(pool_size=3)(x) x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x) x = layers.MaxPooling2D(pool_size=2)(x) x = layers.Conv2D(filters=128, kernel_size=2, activation="relu")(x) x = layers.Flatten()(x) x = layers.Dense(128, activation="relu")(x) x = layers.Dropout(0.5)(x) outputs = layers.Dense(10, activation="softmax")(x) model = keras.Model(inputs=inputs, outputs=outputs)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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(a). Without running the code in Python, describe the architecture for the following
deep learning model, including number of layers, dimension of images in each layer,
number of parameters in each layer. Show details of all you calculations.
inputs = keras.Input(shape=(36, 36, 1))
x = layers.Conv2D(filters=16, kernel_size=5, activation="relu")(inputs)
x = layers.MaxPooling2D(pool_size=3)(x)
x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=2, activation="relu")(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation="relu")(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
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