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Code introduction
This function uses the Keras library to generate a simple neural network model and make predictions using randomly generated weights and biases. It accepts input shape and number of samples as parameters and returns the model's prediction results.
Technology Stack : The code uses the Keras library and involves the following technologies: Keras (a high-level neural networks API), TensorFlow (an open-source software library for dataflow and differentiable programming across a range of tasks), NumPy (a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays)
Code Type : Function
Code Difficulty : Advanced
def generate_random_vector(input_shape, num_samples):
from tensorflow import keras
import numpy as np
model = keras.Sequential([
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
# Generate random input data
random_input = np.random.random((num_samples, *input_shape))
# Generate random labels
random_labels = np.random.randint(0, 10, size=(num_samples,))
# Generate random weights
random_weights = np.random.random((10, 64))
# Generate random biases
random_biases = np.random.random((10,))
# Set random weights and biases
model.set_weights([random_weights, random_biases])
# Predict using the generated random weights and biases
predictions = model.predict(random_input)
return predictions
