import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
from jax import config
config.update("jax_enable_x64", False)
plt.rcParams['font.family'] = 'Noto Sans CJK JP'
data_label = np.load("tl_freq_data.npz")
data = data_label["data"].reshape(-1,200,5,1)
label = data_label["label"]
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.3, random_state=0)
y_train_list = [y_train[:,:,1], y_train[:,:,2]]
y_test_list = [y_test[:,:,1], y_test[:,:,2]]
scaler_y = [StandardScaler() for _ in range(2)]
y_train_list_f = [None for _ in range(2)]
for i in range(2):
scaler_y[i] = StandardScaler()
y_train_list_f[i] = scaler_y[i].fit_transform(y_train_list[i])
inputs = keras.Input(shape=(200, 5, 1))
x = keras.layers.Conv2D(64, kernel_size=(10, 2), activation="relu")(inputs)
x = keras.layers.Conv2D(64, kernel_size=(10, 4), activation="relu")(x)
x = keras.layers.Flatten()(x)
outputs = [keras.layers.Dense(200)(x) for i in range(2)]
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss = 'mean_squared_error' ,optimizer=keras.optimizers.Adam())
batch_size = 64
epochs = 1000
keras.utils.set_random_seed(1)
history = model.fit(
x_train,
y_train_list_f,
batch_size=batch_size,
epochs=epochs,
validation_split=0.15,
)
y_pred_list_f = model.predict(x_test)
y_pred_list = [None for _ in range(2)]
for i in range(2):
y_pred_list[i] = scaler_y[i].inverse_transform(y_pred_list_f[i])
metric = keras.metrics.R2Score()
for idx in range(2):
print(f"R2 score for label {idx}:")
metric.update_state(y_test_list[idx], y_pred_list[idx])
result = metric.result()
print(result)
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