Perkinson CNN Train accuracy test abed

import pandas as pd

from main import classifier, test_set

df = pd.read_csv('data.csv')
# print(df)

dataset = df.values
#
# print(dataset)

X = dataset[:, 0:10]
Y = dataset[:, 10]

from sklearn import preprocessing

min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)

# print(X_scale)

from sklearn.model_selection import train_test_split

X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
print(X_train.shape, X_val.shape, X_test.shape, Y_train.shape, Y_val.shape, Y_test.shape)


from keras.models import Sequential
from keras.layers import Dense


model = Sequential([
    Dense(32, activation='relu', input_shape=(10,)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid'),
])

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

hist = model.fit(X_train, Y_train,
          batch_size=32, epochs=100,
          validation_data=(X_val, Y_val))

model.evaluate(X_test, Y_test)[1]

score = model.evaluate(X_test, Y_test, verbose=0)
# print('Test loss:', score[0])
print('Test accuracy:', score[1])