Lehren, wie Sie Schaltjahre für ein Lernmodell identifizieren können ⇐ Python

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Ich versuche mein Lernmodell beizubringen, wie ich Schaltjahre identifizieren kann.import tensorflow as tf
import numpy as np
import calendar

# ------------------- UTILITY FUNCTIONS -------------------
def is_leap(year):
"""Checks if a year is a leap year."""
return calendar.isleap(year)

def generate_data(num_samples):
"""
Generates training data and labels. The generated data are random numbers starting from 1000.
Two types of data are returned, one for training and one for validation. Both are numpy arrays.
"""
inputs = []
outputs = []

for i in range(num_samples):
inputs.append(np.random.randint(10000, 20001))

if is_leap(inputs):
outputs.append(1)
else:
outputs.append(0)

# Lists are converted to numpy arrays.
return np.array(inputs, dtype='float32'), np.array(outputs, dtype='float32')

# ------------------- MODEL CREATION AND TRAINING -------------------
def create_model():
"""Creates a sequential Keras model."""
model = tf.keras.models.Sequential([
tf.keras.layers.Input(shape=(1,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model

def train_model(model, inputs, outputs, epochs=20):
"""Trains the model."""
model.fit(inputs, outputs, epochs=epochs)

# ------------------- MAIN -------------------
if __name__ == "__main__":
# Generates data for model training.
num_samples = 10000
years, results = generate_data(num_samples)

# Model creation and training.
model = create_model()
train_model(model, years, results, 60)

# Years for testing
test_years, test_results = generate_data(100)

# Gets predictions.
predictions = model.predict(test_years)

# Prints the results
print("---------- MODEL TEST ----------")

for i in range(test_years.shape[0]):
prediction = predictions[0]
prediction_01 = 1 if prediction
Ich habe mehrere verschiedene Konfigurationen ausprobiert, aber das Ergebnis scheint immer verzerrt zu sein, entweder zeigt, dass alle Testjahre Jahre oder das Gegenteil sind. Hier ist ein Beispiel für die Ausgabe des Programms mit den im Code gezeigten Einstellungen: < /p>
---------- MODEL TEST ----------
[15782.0]: Prediction: 0.028894491493701935 (1), Real: 0.0
[12486.0]: Prediction: 0.056425176560878754 (1), Real: 0.0
[19711.0]: Prediction: 0.012782025150954723 (1), Real: 0.0
[11396.0]: Prediction: 0.07004968822002411 (1), Real: 1.0
[14363.0]: Prediction: 0.03863195702433586 (1), Real: 0.0
[17586.0]: Prediction: 0.01990177109837532 (1), Real: 0.0
[18930.0]: Prediction: 0.015046692453324795 (1), Real: 0.0
[10777.0]: Prediction: 0.07908520102500916 (1), Real: 0.0
[14515.0]: Prediction: 0.03745480626821518 (1), Real: 0.0
[12179.0]: Prediction: 0.059987716376781464 (1), Real: 0.0
[11645.0]: Prediction: 0.06669164448976517 (1), Real: 0.0
[13158.0]: Prediction: 0.049309246242046356 (1), Real: 0.0
[11396.0]: Prediction: 0.07004968822002411 (1), Real: 1.0
[12925.0]: Prediction: 0.0516749769449234 (1), Real: 0.0
[14807.0]: Prediction: 0.035287827253341675 (1), Real: 0.0
[15088.0]: Prediction: 0.03331705927848816 (1), Real: 1.0
[16337.0]: Prediction: 0.025773070752620697 (1), Real: 0.0
[19299.0]: Prediction: 0.013931039720773697 (1), Real: 0.0
[16406.0]: Prediction: 0.025408802554011345 (1), Real: 0.0
[13144.0]: Prediction: 0.0494486503303051 (1), Real: 1.0
[18829.0]: Prediction: 0.015366936102509499 (1), Real: 0.0
[15150.0]: Prediction: 0.03289690986275673 (1), Real: 0.0
[17999.0]: Prediction: 0.018265796825289726 (1), Real: 0.0
[11613.0]: Prediction: 0.06711487472057343 (1), Real: 0.0
[13565.0]: Prediction: 0.04542219638824463 (1), Real: 0.0
[10702.0]: Prediction: 0.0802496150135994 (1), Real: 0.0
[13246.0]: Prediction: 0.048442721366882324 (1), Real: 0.0
[18801.0]: Prediction: 0.015456832014024258 (1), Real: 0.0
[19585.0]: Prediction: 0.013123226352036 (1), Real: 0.0
[17522.0]: Prediction: 0.020168287679553032 (1), Real: 0.0
[15651.0]: Prediction: 0.029683079570531845 (1), Real: 0.0
[16680.0]: Prediction: 0.024010933935642242 (1), Real: 1.0
[15311.0]: Prediction: 0.03182867914438248 (1), Real: 0.0
[12538.0]: Prediction: 0.055841829627752304 (1), Real: 0.0
[10095.0]: Prediction: 0.09026535600423813 (1), Real: 0.0
[12790.0]: Prediction: 0.05309422314167023 (1), Real: 0.0
[16811.0]: Prediction: 0.023368824273347855 (1), Real: 0.0
[11217.0]: Prediction: 0.0725601464509964 (1), Real: 0.0
[14563.0]: Prediction: 0.03708960860967636 (1), Real: 0.0
[14162.0]: Prediction: 0.04024471715092659 (1), Real: 0.0
[12618.0]: Prediction: 0.05495523288846016 (1), Real: 0.0
[19699.0]: Prediction: 0.012814437039196491 (1), Real: 0.0
[16408.0]: Prediction: 0.02539803832769394 (1), Real: 1.0
[14447.0]: Prediction: 0.037976961582899094 (1), Real: 0.0
[15228.0]: Prediction: 0.03237523138523102 (1), Real: 1.0
[15779.0]: Prediction: 0.02891264297068119 (1), Real: 0.0
[11751.0]: Prediction: 0.06530816853046417 (1), Real: 0.0
[13550.0]: Prediction: 0.04556054621934891 (1), Real: 0.0
[11842.0]: Prediction: 0.06414111703634262 (1), Real: 0.0
[17247.0]: Prediction: 0.021351758390665054 (1), Real: 0.0
[17256.0]: Prediction: 0.021312057971954346 (1), Real: 1.0
[10513.0]: Prediction: 0.08325573056936264 (1), Real: 0.0
[15134.0]: Prediction: 0.03300463408231735 (1), Real: 0.0
[17974.0]: Prediction: 0.01836095005273819 (1), Real: 0.0
[10549.0]: Prediction: 0.08267498016357422 (1), Real: 0.0
[13928.0]: Prediction: 0.04220344498753548 (1), Real: 1.0
[16993.0]: Prediction: 0.022505149245262146 (1), Real: 0.0
[17120.0]: Prediction: 0.021921301260590553 (1), Real: 1.0
[14685.0]: Prediction: 0.03617788851261139 (1), Real: 0.0
[11489.0]: Prediction: 0.06877779960632324 (1), Real: 0.0
[17720.0]: Prediction: 0.01935615763068199 (1), Real: 1.0
[11165.0]: Prediction: 0.07330425828695297 (1), Real: 0.0
[13673.0]: Prediction: 0.0444408617913723 (1), Real: 0.0
[10098.0]: Prediction: 0.09021187573671341 (1), Real: 0.0
[13159.0]: Prediction: 0.049299370497465134 (1), Real: 0.0
[17958.0]: Prediction: 0.018422311171889305 (1), Real: 0.0
[15849.0]: Prediction: 0.02849932760000229 (1), Real: 0.0
[19562.0]: Prediction: 0.013186565600335598 (1), Real: 0.0
[12582.0]: Prediction: 0.05535256117582321 (1), Real: 0.0
[18768.0]: Prediction: 0.015563310123980045 (1), Real: 1.0
[16677.0]: Prediction: 0.024025622755289078 (1), Real: 0.0
[10858.0]: Prediction: 0.07784521579742432 (1), Real: 0.0
[12186.0]: Prediction: 0.05990469083189964 (1), Real: 0.0
[18558.0]: Prediction: 0.016259854659438133 (1), Real: 0.0
[19349.0]: Prediction: 0.013786446303129196 (1), Real: 0.0
[17064.0]: Prediction: 0.022176457569003105 (1), Real: 1.0
[18551.0]: Prediction: 0.016283303499221802 (1), Real: 0.0
[15677.0]: Prediction: 0.029525138437747955 (1), Real: 0.0
[14678.0]: Prediction: 0.036229733377695084 (1), Real: 0.0
[15669.0]: Prediction: 0.029574228450655937 (1), Real: 0.0
[17092.0]: Prediction: 0.022048931568861008 (1), Real: 1.0
[13242.0]: Prediction: 0.04848231002688408 (1), Real: 0.0
[11103.0]: Prediction: 0.07420048862695694 (1), Real: 0.0
[18457.0]: Prediction: 0.01660582609474659 (1), Real: 0.0
[14844.0]: Prediction: 0.035021863877773285 (1), Real: 1.0
[11382.0]: Prediction: 0.07024302333593369 (1), Real: 0.0
[18430.0]: Prediction: 0.016699181869626045 (1), Real: 0.0
[17603.0]: Prediction: 0.019832022488117218 (1), Real: 0.0
[15078.0]: Prediction: 0.03338494151830673 (1), Real: 0.0
[17369.0]: Prediction: 0.02081885002553463 (1), Real: 0.0
[14098.0]: Prediction: 0.04077168554067612 (1), Real: 0.0
[16942.0]: Prediction: 0.02274395152926445 (1), Real: 0.0
[17134.0]: Prediction: 0.021857596933841705 (1), Real: 0.0
[18702.0]: Prediction: 0.01577908731997013 (1), Real: 0.0
[18461.0]: Prediction: 0.016592128202319145 (1), Real: 0.0
[15036.0]: Prediction: 0.033673398196697235 (1), Real: 1.0
[12185.0]: Prediction: 0.059916503727436066 (1), Real: 0.0
[11637.0]: Prediction: 0.06679684668779373 (1), Real: 0.0
[16475.0]: Prediction: 0.025049572810530663 (1), Real: 0.0
[12810.0]: Prediction: 0.052881550043821335 (1), Real: 0.0
< /code>
Wenn ich die Epochen erhöhe, werden die Werte sehr niedrig, ungefähr 0,00025 und sie wiederholen sich weiterhin. Wenn ich die Epochen verringere, werden sie zu hoch. Ich kann keine zufriedenstellenden Ergebnisse finden oder das Modell richtig trainieren. < /P>
Ich habe versucht, die Werte zu standardisieren, aber es hat nicht funktioniert. Ich weiß nicht, was ich sonst noch tun kann. Könnte mir jemand helfen?