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replicate_v1.py
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replicate_v1.py
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;; Happy hacking, Nasy - Emacs ♥ you!
from csv import reader, writer
from itertools import chain
from tqdm import tqdm
from numpy import genfromtxt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow import keras
def read_row_dataset(path = "./waveforms_with_paraName.csv"):
with open(path, newline="") as f:
next(f)
for row in reader(f):
yield row[1:18607], row[18607:]
with open(f"wave_x", "w") as xf, open(f"wave_y", "w") as yf:
xw = writer(xf)
yw = writer(yf)
for x, y in tqdm(read_row_dataset()):
xw.writerow(x)
yw.writerow(y)
def read_dataset(prefix = "wave"):
return train_test_split(genfromtxt(f"{prefix}_x", delimiter = ","), genfromtxt(f"{prefix}_y", delimiter = ","))
train_x, test_x, train_y, test_y = read_dataset()
stdxs = StandardScaler().fit(train_x)
stdys = StandardScaler().fit(train_y)
train_x = np.expand_dims(stdxs.transform(train_x), axis=1)
train_y = stdys.transform(train_y)
test_x = np.expand_dims(stdxs.transform(test_x), axis=1)
test_y = stdys.transform(test_y)
print(train_x.shape)
model = keras.Sequential(
[
#keras.Sequential([
keras.layers.Conv1D(filters = 64, kernel_size = 16, strides = 1, padding = "causal", input_shape = (1, 18606)),
keras.layers.MaxPool1D(pool_size = 4, strides = 4, padding = "same"),
keras.layers.ReLU(),
# ]),
# keras.Sequential([
keras.layers.Conv1D(filters = 128, kernel_size = 16, strides = 1, padding = "causal"),
keras.layers.MaxPooling1D(pool_size = 4, strides = 4, padding = "same"),
keras.layers.ReLU(),
# ]),
# keras.Sequential([
keras.layers.Conv1D(filters = 256, kernel_size = 16, strides = 1, padding = "causal"),
keras.layers.MaxPooling1D(pool_size = 4, strides = 4, padding = "same"),
keras.layers.ReLU(),
# ]),
# keras.Sequential([
keras.layers.Conv1D(filters = 512, kernel_size = 32, strides = 1, padding = "causal"),
keras.layers.MaxPooling1D(pool_size = 4, strides = 4, padding = "same"),
keras.layers.ReLU(),
# ]),
keras.layers.Flatten(),
keras.layers.Dense(128, activation = "relu"),
keras.layers.Dense(64, activation = "relu"),
keras.layers.Dense(9, activation = "relu"),
]
)
model.compile(optimizer='adam', loss='mse')
print(model.summary())
model.fit(train_x, train_y)
p = model.predict(test_x)
result = stdys.inverse_transform(p)
print(result, stdys.inverse_transform(test_y))