mirror of
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113 lines
2.4 KiB
Python
113 lines
2.4 KiB
Python
# %%
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import json
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import os
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import sys
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import numpy as np
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import pandas as pd
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import tensorflow as tf
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from keras.layers import Dense
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from keras.models import Sequential
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from keras.utils import to_categorical
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from sklearn.model_selection import train_test_split
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from tensorflow import keras
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from pso import optimizer
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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def get_data():
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with open("data/seeds/seeds_dataset.txt", "r", encoding="utf-8") as f:
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data = f.readlines()
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df = pd.DataFrame([d.split() for d in data])
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df.columns = [
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"area",
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"perimeter",
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"compactness",
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"length_of_kernel",
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"width_of_kernel",
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"asymmetry_coefficient",
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"length_of_kernel_groove",
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"target",
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]
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df = df.astype(float)
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df["target"] = df["target"].astype(int)
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x = df.iloc[:, :-1].values.round(0).astype(int)
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y = df.iloc[:, -1].values
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y_class = to_categorical(y)
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x_train, x_test, y_train, y_test = train_test_split(
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x, y_class, test_size=0.2, shuffle=True
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)
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return x_train, y_train, x_test, y_test
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def make_model():
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model = Sequential()
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model.add(Dense(16, activation="relu", input_shape=(7,)))
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model.add(Dense(32, activation="relu"))
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model.add(Dense(4, activation="softmax"))
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return model
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# %%
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model = make_model()
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x_train, y_train, x_test, y_test = get_data()
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loss = [
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"mean_squared_error",
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"categorical_crossentropy",
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"sparse_categorical_crossentropy",
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"binary_crossentropy",
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"kullback_leibler_divergence",
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"poisson",
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"cosine_similarity",
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"log_cosh",
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"huber_loss",
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"mean_absolute_error",
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"mean_absolute_percentage_error",
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]
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# rs = random_state()
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pso_mnist = optimizer(
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model,
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loss="categorical_crossentropy",
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n_particles=100,
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c0=0.5,
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c1=1.0,
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w_min=0.7,
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w_max=1.2,
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negative_swarm=0.0,
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mutation_swarm=0.3,
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convergence_reset=True,
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convergence_reset_patience=10,
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convergence_reset_monitor="mse",
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convergence_reset_min_delta=0.0005,
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)
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best_score = pso_mnist.fit(
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x_train,
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y_train,
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epochs=500,
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save_info=True,
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log=2,
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log_name="seeds",
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renewal="acc",
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check_point=25,
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empirical_balance=False,
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dispersion=False,
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back_propagation=False,
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validate_data=(x_test, y_test),
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)
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print("Done!")
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sys.exit(0)
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