import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
import jax
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
plt.rcParams['font.family'] = 'Noto Sans CJK JP'
from jax import config
config.update("jax_enable_x64", False)
data_label = np.load("complex_tl_data.npz")
data = data_label["data"]
label = data_label["label"]
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.3, random_state=0)
#正規化
scaler_y = StandardScaler()
y_train_f = scaler_y.fit_transform(y_train)
y_test_f = scaler_y.transform(y_test)
# Functional APIでDense層を3層にしたDNNを設定
hidden_dim = 200
inputs = keras.Input(shape=(200, 5))
x = keras.layers.Flatten()(inputs)
x = keras.layers.Dense(hidden_dim, activation="relu")(x)
x = keras.layers.LayerNormalization(epsilon=1.0e-6)(x)
x = keras.layers.Dense(hidden_dim, activation="relu")(x)
x = keras.layers.LayerNormalization(epsilon=1.0e-6)(x)
x = keras.layers.Dense(hidden_dim, activation="relu")(x)
x = keras.layers.LayerNormalization(epsilon=1.0e-6)(x)
x = keras.layers.Dense(hidden_dim, activation="relu")(x)
x = keras.layers.LayerNormalization(epsilon=1.0e-6)(x)
outputs = keras.layers.Dense(2)(x)
# モデルの設定
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss = 'mean_squared_error' ,optimizer=keras.optimizers.Adam())
batch_size = 128
epochs = 500
keras.utils.set_random_seed(1)
history = model.fit(
x_train,
y_train_f,
batch_size=batch_size,
epochs=epochs,
validation_split=0.15,
)
y_pred_f = model.predict(x_test)
y_pred = scaler_y.inverse_transform(y_pred_f)
metric = keras.metrics.R2Score()
metric.update_state(y_test, y_pred)
result = metric.result()
print(result)
error = np.abs((y_test - y_pred)/y_test*100)
print(error.mean(axis=0))
legend = ["Z0"]
fig, ax = plt.subplots(2, 2, figsize=(12,12))
maxrvalue = y_pred[:,0].max()
minrvalue = y_pred[:,0].min()
maxivalue = y_pred[:,1].max()
minivalue = y_pred[:,1].min()
ax[0, 0].scatter(y_pred[:,0], y_test[:,0], c="r", s=5)
ax[0, 0].plot([0,maxrvalue], [0,maxrvalue], "--", c="black")
ax[0, 0].set_xlabel("推定した値（実部）")
ax[0, 0].set_ylabel("実際の値（実部）")
ax[0, 0].set_xlim(minrvalue, maxrvalue)
ax[0, 0].set_ylim(minrvalue, maxrvalue)
ax[0, 0].grid()
ax[0, 0].legend([legend[0] + f" 平均誤差（実部）{error.mean(axis=0)[0]:.2f}%"])
ax[1, 0].hist(error[:, 0], bins = 100)
ax[1, 0].set_xlabel("誤差[%]（実部）")
ax[1, 0].set_ylabel("頻度")
ax[1, 0].grid()
ax[0, 1].scatter(y_pred[:,1], y_test[:,1], c="r", s=5)
ax[0, 1].plot([0,maxivalue], [0,maxivalue], "--", c="black")
ax[0, 1].set_xlabel("推定した値（虚部）")
ax[0, 1].set_ylabel("実際の値（虚部）")
ax[0, 1].set_xlim(minivalue, maxivalue)
ax[0, 1].set_ylim(minivalue, maxivalue)
ax[0, 1].grid()
ax[0, 1].legend([legend[0] + f" 平均誤差（虚部）{error.mean(axis=0)[1]:.2f}%"])
ax[1, 1].hist(error[:, 1], bins = 100)
ax[1, 1].set_xlabel("誤差[%]（虚部）")
ax[1, 1].set_ylabel("頻度")
ax[1, 1].grid()
fig.tight_layout()
plt.show()
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