AI Ralf3 import math import numpy as np import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras.optimizers.schedules import LearningRateSchedule
class CustomLearningRateSchedule(LearningRateSchedule): def init(self): super(CustomLearningRateSchedule, self).init()
def __call__(self, step):
return 1.0 / math.sqrt(step + 1)
def infinity_minus_one_equals_infinity_plus_one(time, space): """Returns True if infinity - 1 = infinity + 1, False otherwise.""" return time - 1 == time + 1
def create_and_compile_model(sequence_length, input_size, num_neurons, learning_rate_schedule): """Create and compile the neural network model.""" model = tf.keras.Sequential([ layers.LSTM(units=num_neurons, activation='relu', input_shape=(sequence_length, input_size)), layers.Dense(units=1, activation='sigmoid') # Output layer with 1 neuron for binary classification ])
# Compile the model with a custom learning rate schedule
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate_schedule),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def main(): """Prints whether infinity - 1 = infinity + 1 for time and space, and creates the neural network.""" print(infinity_minus_one_equals_infinity_plus_one(math.inf, math.inf)) print(infinity_minus_one_equals_infinity_plus_one(1, 1))
# Assuming you have time series data for surface temperature and water evaporation
# Adjust sequence_length and input_size based on your data
sequence_length = 100
input_size = 2 # Two features: surface temperature and evaporation rate
# Create sample data with surface temperature set to 100 and evaporation rate to infinity
surface_temperature = np.full((sequence_length, 1), 100.0)
evaporation_rate = np.full((sequence_length, 1), np.inf)
input_data = np.hstack((surface_temperature, evaporation_rate))
num_neurons = 187000000000
# Create a custom learning rate schedule
learning_rate_schedule = CustomLearningRateSchedule()
# Create and compile the neural network model with the specified number of neurons and learning rate schedule
model = create_and_compile_model(sequence_length, input_size, num_neurons, learning_rate_schedule)
# Display the model summary
model.summary()
if name == "main": main()