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df=pd.read_csv('glass.csv')

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import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt 
import tensorflow as tf
from tensorflow import keras

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y_pred=classifier.predict(X_test)
y_pred=y_pred>0.5
y_pred_int = y_pred.astype(int)
y_pred_int[:10]

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plot_learningCurve(history, 15)

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def plot_learningCurve(history, epoch):
  # Plot training & validation accuracy values
  epoch_range = range(1, epoch+1)
  plt.plot(epoch_range, history.history['accuracy'])
  plt.plot(epoch_range, history.history['val_accuracy'])
  plt.title('Model accuracy')
  plt.ylabel('Accuracy')
  plt.xlabel('Epoch')
  plt.legend(['Train', 'Val'], loc='upper left')
  plt.show()

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loss, accuracy = classifier.evaluate(X_test, y_test)
print('Accuracy: %.2f' % (accuracy*100))
print('Loss: %.2f' % (loss*100))

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history = classifier.fit(X_train, y_train, epochs=15, validation_data=(X_test, y_test), verbose=1)

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classifier.summary()

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classifier.compile(optimizer='sgd',loss='binary_crossentropy',metrics=['accuracy'])

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classifier=Sequential()
classifier.add(Dense(64,activation='relu',input_dim=117))
classifier.add(Dropout(0.4))
classifier.add(Dense(32,activation='relu'))
classifier.add(Dropout(0.3))
classifier.add(Dense(2,activation='softmax'))