I made a simple model to train my data set which consists of (210 samples and each sample consists of a numpy array of 22 values)
y_trian look like:
and this is my simple code:
import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Activation, Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import categorical_crossentropy import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split from sklearn.utils import shuffler from google.colab import files uploaded = files.upload() import io dset = pd.read_csv(io.BytesIO(uploaded['1-210.csv'])) y= dset.Readernumber x=dset.drop('Readername',axis=1) #the split ratio of 80:20. The 20% testing data set is represented by the 0.2 at the end. x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2) x_train= np.asarray(x_train).astype('float32') y_train = np.asarray(y_train).astype('float32') y_train, x_train = shuffle(y_train, x_train) #create the model #input_shape=(23,) model = Sequential([ Dense(units=4,input_shape=(22,), activation='relu'), Dense(units=16, activation='relu'), Dense(units=10, activation='softmax') ]) #get the model ready for training is call the compile() function on it. model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy']) #train it using the fit() function. model.fit(x_train, y_train, epochs=5)
And this is what I'm getting for all the epochs :
I will be grateful to anyone who can help me!