Python Colaboratory
全結合モデル結果表示ファイルまとめ
全結合モデルファイル keras-test-53.ipynb
に結果表示を追加しました。
合体ファイル名を
全結合モデル結果表示ファイル
keras-test-54.ipynb
とすると次のようになります。
マ－ジ結合した部分は赤字にしています。
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tensorflow import keras
xl_df = pd.read_csv("drive/MyDrive/Colab Notebooks/my_data/colab-data.csv")
day = xl_df["Date"].values
raw_data0 = xl_df["Value"].values
print("raw_data0.shape:", raw_data0.shape)
print("raw_data0")
print(raw_data0)
plt.plot(range(len(raw_data0)), raw_data0)
plt.show()
raw_data = raw_data0.copy()
# 行列の平均、標準偏差を求めます。
mean = np.mean(raw_data)
print("Mean", mean)
raw_data -= mean
std = np.std(raw_data)
print("Std ",std)
# 標準偏差値に変換
raw_data /= std
print("各標準偏差値")
print(raw_data)
# 連続デ－タとする。一つおきは、2
sampling_rate = 1
# 過去20間隔デ－タをひとまとまりとして時系列予測する
sequence_length = 20
delay = sampling_rate * sequence_length
print("delay:", delay)
batch_size = 32 # 適当
# 検証デ－タのスタ－ト値
num_half_samples = int(0.5 * len(raw_data))
train_dataset = keras.utils.timeseries_dataset_from_array(
raw_data,
targets=raw_data[delay:],
sampling_rate=sampling_rate,
sequence_length=sequence_length,
batch_size=batch_size,
)
val_dataset = keras.utils.timeseries_dataset_from_array(
raw_data[:-1],
targets=raw_data[delay:],
sampling_rate=sampling_rate,
sequence_length=sequence_length,
batch_size=batch_size,
start_index=num_half_samples,
)
test_dataset = keras.utils.timeseries_dataset_from_array(
raw_data,
targets=None,
sampling_rate=sampling_rate,
sequence_length=sequence_length,
batch_size=batch_size,
)
# numpy ndarray 配列に変換して表示
# 訓練デ－タセット表示
itr = 0
for samples, targets in train_dataset:
samples_n = samples.numpy()
targets_n = targets.numpy()
if itr == 0:
print("Start in-train:", samples_n[0])
print("Start tar-train:", targets_n[0])
itr = itr + 1
print("End in-train:", samples_n[-1])
print("End tar-train:", targets_n[-1])
# 検証デ－タセット表示
itv = 0
for samples_v, targets_v in val_dataset:
samples_vn = samples_v.numpy()
targets_vn = targets_v.numpy()
if itv == 0:
print("Start in-val:", samples_vn[0])
print("Start tar-val:", targets_vn[0])
itv = itv + 1
print("End in-val:", samples_vn[-1])
print("End tar-val:", targets_vn[-1])
# テストデ－タセット表示
i = 0
for inputs_t in test_dataset:
inputs_n = inputs_t.numpy()
if i == 0:
print("Start test:", inputs_n[0])
i = i + 1
print("End test:", inputs_n[-1])
#------ここまで keras-test-51.ipynb
# from tensorflow import keras
from keras import layers
from keras import initializers
sequence_length = 20
inputs = keras.Input(shape=(sequence_length,))
x = layers.Flatten()(inputs)
x = layers.Dense(
20,
activation="tanh",
kernel_initializer='zeros'
)(x)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
print("モデルア－キテクチャ")
print(model.summary())
#---ここまで keras-test-52.ipynb
callbacks_list = [
keras.callbacks.EarlyStopping(
monitor="val_loss",
patience=2,
),
keras.callbacks.ModelCheckpoint(
"drive/MyDrive/Colab Notebooks/my_data/jena_dense.keras",
save_best_only=True,
)
]
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
history = model.fit(
train_dataset,
epochs=40,
# verbose=0,
validation_data=val_dataset,
callbacks=callbacks_list)
loss = history.history["mae"] # 平均絶対誤差(MAE)
val_loss = history.history["val_mae"]
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, "bo", label="Training MAE")
plt.plot(epochs, val_loss, "b", label="Validation MAE")
plt.title("Training and Validation MAE")
plt.legend()
plt.show()
#---ここまで keras-test-53.ipynb
model = keras.models.load_model(
"drive/MyDrive/Colab Notebooks/my_data/jena_dense.keras")
pre = model.predict(test_dataset)
pre1 = np.reshape(pre, (-1))
print(f"Test 予測値:")
print(pre1)
future_test = inputs_t[-1:]
print("future_test 最初の配列値")
print(future_test)
future_result = []
for i in range(5):
test_data_f = np.reshape(future_test, (1, 20, 1))
batch_predict = model.predict(test_data_f)
future_test = np.delete(future_test, 0)
future_test = np.append(future_test, batch_predict)
future_result = np.append(future_result, batch_predict)
print("future_result :")
print(future_result)
# ここまで colab-21 まとめ
len_raw_data = len(raw_data)
xx1 = np.arange(sequence_length, len_raw_data + 1)
xx3 = np.arange(len_raw_data, len_raw_data + 5)
plt.plot(xx1, pre)
plt.plot(xx3, future_result)
plt.show()
#ここまで colab-22①まとめ
pre_chg = pre.copy()
pre_chg *= std
pre_chg += mean
pre_chg1 = np.reshape(pre_chg, (-1))
print("pre_chg1:")
print(pre_chg1)
f_result = future_result.copy()
f_result *= std
f_result += mean
print("f_result:", f_result)
plt.plot(range(len(raw_data0)), raw_data0)
plt.plot(xx1, pre_chg)
plt.plot(xx3, f_result)
plt.show()
#ここまで colab-22②まとめ