Thank you for the reply.
First of all, let’s talk about the dataset. This is a self-created dataset, as shown in the image below.
It consists of label information and 7 parameters. To explain the data in more detail, Columns 1, 2, and 3 operate like a for statement.
for _ in columns1 :
for _ in columns2 :
for _ in columns3 :
The values in columns 4 to 7 are measurement values for the operations in columns 1 to 3.
The amount of data set is approximately 550,000, so it is believed that there are no issues with the amount.
I am aware that there may be problems with the data (for example, the dataset structure is incorrect).
Then I added the code below.
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Dense, Flatten, Conv1D, MaxPool1D
from tensorflow.keras.losses import binary_crossentropy
df = pd.read_csv(‘./…/…/…/…/col_rx_dataset_rev1.csv’)
df.head(5)
df_columns = df.columns.unique()
train_data = df[df_columns[1:]]
label_data = df[df_columns[0]]
print(label_data[:10])
x_train, x_test, y_train, y_test = train_test_split(train_data, label_data, test_size= 0.3, random_state=42)
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size= 0.4, random_state=42)
model = Sequential()
model.add(Flatten(input_shape=(7,1)))
model.add(Dense(256, activation=‘relu’))
model.add(Dense(128, activation=‘relu’))
model.add(Dense(64, activation=‘relu’))
model.add(Dense(64, activation=‘relu’))
model.add(Dense(1, activation=‘sigmoid’))
model.compile(optimizer=‘adam’,
loss=‘binary_crossentropy’,
metrics=[‘accuracy’])
model.fit(x_train, y_train,validation_data=(x_valid,y_valid), epochs=100, batch_size=64, verbose=1)
I plan to add a Feature Extraction layer after checking how much learning is possible with the MLP model. However, in my simple MLP, there was no change in accuracy.
