We trained a binary classification nn with ~90% accuracy/~20% loss. When we use the model by calling model.predict() function with the testing data (which is 20% of our entire dataset), we get a relatively even distribution of 1's and 0's. But when we input individual points from the testing data as a numpy array, we only get 0's regardless of shuffling or not. Can anyone help us explain why we are getting this behavior?
When we use X_test (from the split above) instead of dummyTest, binary_prediction equals 1 for the respective data point array from dummyTest), but when we use dummyTest's data individually, binary_prediction only equals 0.
Full Code Shown Below:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
from sklearn import model_selection
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from keras.layers import Dropout
import os
full_dataset = pd.read_csv('noQMarkDataset.csv')
data = full_dataset[~full_dataset.isin(['?'])]
data = data.dropna(axis=0)
data = data.apply(pd.to_numeric)
X = np.array(data.drop(['target'], 1))
y = np.array(data['target'])
mean = X.mean(axis=0)
X -= mean
std = X.std(axis=0)
X /= std
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, random_state = 0, test_size = 0.2, shuffle=False) #stratify = y
# convert data to categorical labels
Y_train = to_categorical(y_train, num_classes=None)
Y_test = to_categorical(y_test, num_classes=None)
Y_train_binary = y_train.copy()
Y_test_binary = y_test.copy()
Y_train_binary[Y_train_binary > 0] = 1
Y_test_binary[Y_test_binary > 0] = 1
def create_binary_model():
model = Sequential()
model.add(Dense(16, input_dim=7, activation='relu')) # prev input_dim =1 3
model.add(Dropout(0.25))
model.add(Dense(16, activation = 'relu'))
model.add(Dropout(0.25))
model.add(Dense(16))
model.add(Dropout(0.25))
model.add(Dense(12, activation = 'relu'))
model.add(Dense(12, activation = 'relu'))
model.add(Dropout(0.25))
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))
adam = Adam(lr=0.001)
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) #optimizer = 'rmsprop' previously
return model
binary_model = create_binary_model()
history=binary_model.fit(X_train, Y_train_binary, validation_data=(X_test, Y_test_binary), epochs=130, batch_size=15) #epochs = 130, batch_size = 20 previously (best)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'])
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'])
plt.show()
from sklearn.metrics import classification_report, accuracy_score
dummyTest = np.array([[67.0,1.0,4.0,0.0,2.0,129.0,2.0]])
binary_pred = np.round(binary_model.predict(dummyTest)).astype(int)
#binary_pred = np.round(binary_model.predict(X_test)).astype(int)
print(f"Binary Pred: {binary_pred}")
binary_model.save(os.path.join(".", "test_model.h5"))
#after loading
from tensorflow.keras.models import load_model
model2 = load_model(os.path.join(".", "test_model.h5"))
binary_pred2 = np.round(binary_model.predict(dummyTest)).astype(int)
#binary_pred2 = np.round(binary_model.predict(X_test)).astype(int)
print(f"Binary Pred2 after load: {binary_pred2}")
Full Dataset Shown Below
age,sex,cp,fbs,restecg,thalach,ca,target
63.0,1.0,1.0,1.0,2.0,150.0,0.0,0
67.0,1.0,4.0,0.0,2.0,108.0,3.0,2
67.0,1.0,4.0,0.0,2.0,129.0,2.0,1
37.0,1.0,3.0,0.0,0.0,187.0,0.0,0
41.0,0.0,2.0,0.0,2.0,172.0,0.0,0
56.0,1.0,2.0,0.0,0.0,178.0,0.0,0
62.0,0.0,4.0,0.0,2.0,160.0,2.0,3
57.0,0.0,4.0,0.0,0.0,163.0,0.0,0
63.0,1.0,4.0,0.0,2.0,147.0,1.0,2
53.0,1.0,4.0,1.0,2.0,155.0,0.0,1
57.0,1.0,4.0,0.0,0.0,148.0,0.0,0
56.0,0.0,2.0,0.0,2.0,153.0,0.0,0
56.0,1.0,3.0,1.0,2.0,142.0,1.0,2
44.0,1.0,2.0,0.0,0.0,173.0,0.0,0
52.0,1.0,3.0,1.0,0.0,162.0,0.0,0
57.0,1.0,3.0,0.0,0.0,174.0,0.0,0
48.0,1.0,2.0,0.0,0.0,168.0,0.0,1
54.0,1.0,4.0,0.0,0.0,160.0,0.0,0
48.0,0.0,3.0,0.0,0.0,139.0,0.0,0
49.0,1.0,2.0,0.0,0.0,171.0,0.0,0
64.0,1.0,1.0,0.0,2.0,144.0,0.0,0
58.0,0.0,1.0,1.0,2.0,162.0,0.0,0
58.0,1.0,2.0,0.0,2.0,160.0,0.0,1
58.0,1.0,3.0,0.0,2.0,173.0,2.0,3
60.0,1.0,4.0,0.0,2.0,132.0,2.0,4
50.0,0.0,3.0,0.0,0.0,158.0,0.0,0
58.0,0.0,3.0,0.0,0.0,172.0,0.0,0
66.0,0.0,1.0,0.0,0.0,114.0,0.0,0
43.0,1.0,4.0,0.0,0.0,171.0,0.0,0
40.0,1.0,4.0,0.0,2.0,114.0,0.0,3
69.0,0.0,1.0,0.0,0.0,151.0,2.0,0
60.0,1.0,4.0,1.0,0.0,160.0,2.0,2
64.0,1.0,3.0,0.0,0.0,158.0,0.0,1
59.0,1.0,4.0,0.0,0.0,161.0,0.0,0
44.0,1.0,3.0,0.0,0.0,179.0,0.0,0
42.0,1.0,4.0,0.0,0.0,178.0,0.0,0
43.0,1.0,4.0,0.0,2.0,120.0,0.0,3
57.0,1.0,4.0,0.0,2.0,112.0,1.0,1
55.0,1.0,4.0,0.0,0.0,132.0,1.0,3
61.0,1.0,3.0,1.0,0.0,137.0,0.0,0
65.0,0.0,4.0,0.0,2.0,114.0,3.0,4
40.0,1.0,1.0,0.0,0.0,178.0,0.0,0
71.0,0.0,2.0,0.0,0.0,162.0,2.0,0
59.0,1.0,3.0,1.0,0.0,157.0,0.0,0
61.0,0.0,4.0,0.0,2.0,169.0,0.0,1
58.0,1.0,3.0,0.0,2.0,165.0,1.0,4
51.0,1.0,3.0,0.0,0.0,123.0,0.0,0
50.0,1.0,4.0,0.0,2.0,128.0,0.0,4
65.0,0.0,3.0,1.0,2.0,157.0,1.0,0
53.0,1.0,3.0,1.0,2.0,152.0,0.0,0
41.0,0.0,2.0,0.0,0.0,168.0,1.0,0
65.0,1.0,4.0,0.0,0.0,140.0,0.0,0
44.0,1.0,4.0,0.0,2.0,153.0,1.0,2
44.0,1.0,2.0,0.0,2.0,188.0,0.0,0
60.0,1.0,4.0,0.0,0.0,144.0,1.0,1
54.0,1.0,4.0,0.0,2.0,109.0,1.0,1
50.0,1.0,3.0,0.0,0.0,163.0,1.0,1
41.0,1.0,4.0,0.0,2.0,158.0,0.0,1
54.0,1.0,3.0,0.0,2.0,152.0,1.0,0
51.0,1.0,1.0,0.0,2.0,125.0,1.0,0
51.0,0.0,4.0,0.0,0.0,142.0,0.0,2
46.0,0.0,3.0,0.0,2.0,160.0,0.0,0
58.0,1.0,4.0,0.0,2.0,131.0,3.0,1
54.0,0.0,3.0,1.0,0.0,170.0,0.0,0
54.0,1.0,4.0,0.0,0.0,113.0,1.0,2
60.0,1.0,4.0,0.0,2.0,142.0,2.0,2
60.0,1.0,3.0,0.0,2.0,155.0,0.0,1
54.0,1.0,3.0,0.0,2.0,165.0,0.0,0
59.0,1.0,4.0,0.0,2.0,140.0,0.0,2
46.0,1.0,3.0,0.0,0.0,147.0,0.0,1
65.0,0.0,3.0,0.0,0.0,148.0,0.0,0
67.0,1.0,4.0,1.0,0.0,163.0,2.0,3
62.0,1.0,4.0,0.0,0.0,99.0,2.0,1
65.0,1.0,4.0,0.0,2.0,158.0,2.0,1
44.0,1.0,4.0,0.0,2.0,177.0,1.0,1
65.0,0.0,3.0,0.0,2.0,151.0,0.0,0
60.0,1.0,4.0,0.0,2.0,141.0,1.0,1
51.0,0.0,3.0,0.0,2.0,142.0,1.0,0
48.0,1.0,2.0,0.0,2.0,180.0,0.0,0
58.0,1.0,4.0,0.0,2.0,111.0,0.0,3
45.0,1.0,4.0,0.0,2.0,148.0,0.0,0
53.0,0.0,4.0,0.0,2.0,143.0,0.0,0
39.0,1.0,3.0,0.0,2.0,182.0,0.0,0
68.0,1.0,3.0,1.0,2.0,150.0,0.0,3
52.0,1.0,2.0,0.0,0.0,172.0,0.0,0
44.0,1.0,3.0,0.0,2.0,180.0,0.0,0
47.0,1.0,3.0,0.0,2.0,156.0,0.0,0
53.0,0.0,3.0,0.0,2.0,115.0,0.0,0
53.0,0.0,4.0,0.0,2.0,160.0,0.0,0
51.0,0.0,3.0,0.0,2.0,149.0,0.0,0
66.0,1.0,4.0,0.0,2.0,151.0,0.0,0
62.0,0.0,4.0,0.0,2.0,145.0,3.0,3
62.0,1.0,3.0,0.0,0.0,146.0,3.0,0
44.0,0.0,3.0,0.0,0.0,175.0,0.0,0
63.0,0.0,3.0,0.0,2.0,172.0,0.0,0
52.0,1.0,4.0,0.0,0.0,161.0,1.0,1
59.0,1.0,4.0,0.0,2.0,142.0,1.0,2
60.0,0.0,4.0,0.0,2.0,157.0,2.0,3
52.0,1.0,2.0,0.0,0.0,158.0,1.0,0
48.0,1.0,4.0,0.0,2.0,186.0,0.0,0
45.0,1.0,4.0,0.0,2.0,185.0,0.0,0
34.0,1.0,1.0,0.0,2.0,174.0,0.0,0
57.0,0.0,4.0,0.0,2.0,159.0,1.0,0
71.0,0.0,3.0,1.0,2.0,130.0,1.0,0
49.0,1.0,3.0,0.0,0.0,139.0,3.0,3
54.0,1.0,2.0,0.0,0.0,156.0,0.0,0
59.0,1.0,4.0,0.0,0.0,162.0,1.0,2
57.0,1.0,3.0,0.0,2.0,150.0,1.0,1
61.0,1.0,4.0,0.0,0.0,140.0,1.0,2
39.0,1.0,4.0,0.0,0.0,140.0,0.0,3
61.0,0.0,4.0,0.0,2.0,146.0,0.0,1
56.0,1.0,4.0,1.0,2.0,144.0,1.0,1
52.0,1.0,1.0,0.0,2.0,190.0,0.0,0
43.0,0.0,4.0,1.0,2.0,136.0,0.0,2
62.0,0.0,3.0,0.0,0.0,97.0,1.0,2
41.0,1.0,2.0,0.0,0.0,132.0,0.0,0
58.0,1.0,3.0,1.0,2.0,165.0,0.0,0
35.0,0.0,4.0,0.0,0.0,182.0,0.0,0
63.0,1.0,4.0,1.0,2.0,132.0,3.0,3
65.0,1.0,4.0,0.0,2.0,127.0,1.0,2
48.0,1.0,4.0,1.0,2.0,150.0,2.0,3
63.0,0.0,4.0,0.0,2.0,154.0,3.0,4
51.0,1.0,3.0,0.0,0.0,143.0,0.0,0
55.0,1.0,4.0,0.0,0.0,111.0,0.0,3
65.0,1.0,1.0,1.0,2.0,174.0,1.0,1
45.0,0.0,2.0,0.0,2.0,175.0,0.0,0
56.0,0.0,4.0,1.0,2.0,133.0,2.0,3
54.0,1.0,4.0,0.0,0.0,126.0,1.0,3
44.0,1.0,2.0,0.0,0.0,170.0,0.0,0
62.0,0.0,4.0,0.0,0.0,163.0,0.0,0
54.0,1.0,3.0,0.0,2.0,147.0,0.0,0
51.0,1.0,3.0,0.0,0.0,154.0,1.0,0
29.0,1.0,2.0,0.0,2.0,202.0,0.0,0
51.0,1.0,4.0,0.0,2.0,186.0,0.0,0
43.0,0.0,3.0,0.0,0.0,165.0,0.0,0
55.0,0.0,2.0,0.0,2.0,161.0,0.0,0
70.0,1.0,4.0,0.0,0.0,125.0,0.0,4
62.0,1.0,2.0,0.0,2.0,103.0,1.0,3
35.0,1.0,4.0,0.0,0.0,130.0,0.0,1
51.0,1.0,3.0,1.0,2.0,166.0,0.0,0
59.0,1.0,2.0,0.0,0.0,164.0,0.0,0
59.0,1.0,1.0,0.0,2.0,159.0,0.0,1
52.0,1.0,2.0,1.0,0.0,184.0,0.0,0
64.0,1.0,3.0,0.0,0.0,131.0,0.0,1
58.0,1.0,3.0,0.0,2.0,154.0,0.0,0
47.0,1.0,3.0,0.0,0.0,152.0,0.0,1
57.0,1.0,4.0,1.0,2.0,124.0,3.0,4
41.0,1.0,3.0,0.0,0.0,179.0,0.0,0
45.0,1.0,2.0,0.0,2.0,170.0,0.0,0
60.0,0.0,3.0,0.0,0.0,160.0,1.0,0
52.0,1.0,1.0,1.0,0.0,178.0,0.0,0
42.0,0.0,4.0,0.0,2.0,122.0,0.0,0
67.0,0.0,3.0,0.0,2.0,160.0,0.0,0
55.0,1.0,4.0,0.0,2.0,145.0,1.0,4
64.0,1.0,4.0,0.0,2.0,96.0,1.0,3
70.0,1.0,4.0,0.0,2.0,109.0,3.0,1
51.0,1.0,4.0,0.0,0.0,173.0,0.0,1
58.0,1.0,4.0,0.0,2.0,171.0,2.0,1
60.0,1.0,4.0,0.0,2.0,170.0,2.0,2
68.0,1.0,3.0,0.0,0.0,151.0,1.0,0
46.0,1.0,2.0,1.0,0.0,156.0,0.0,0
77.0,1.0,4.0,0.0,2.0,162.0,3.0,4
54.0,0.0,3.0,0.0,0.0,158.0,0.0,0
58.0,0.0,4.0,0.0,2.0,122.0,0.0,0
48.0,1.0,3.0,1.0,0.0,175.0,2.0,0
57.0,1.0,4.0,0.0,0.0,168.0,0.0,0
54.0,0.0,2.0,1.0,2.0,159.0,1.0,0
35.0,1.0,4.0,0.0,2.0,156.0,0.0,1
45.0,0.0,2.0,0.0,0.0,138.0,0.0,0
70.0,1.0,3.0,0.0,0.0,112.0,1.0,3
53.0,1.0,4.0,0.0,2.0,111.0,0.0,0
59.0,0.0,4.0,0.0,0.0,143.0,0.0,1
62.0,0.0,4.0,0.0,2.0,157.0,0.0,0
64.0,1.0,4.0,0.0,2.0,132.0,2.0,4
57.0,1.0,4.0,0.0,0.0,88.0,1.0,1
52.0,1.0,4.0,1.0,0.0,147.0,3.0,0
56.0,1.0,4.0,0.0,2.0,105.0,1.0,1
43.0,1.0,3.0,0.0,0.0,162.0,1.0,0
53.0,1.0,3.0,1.0,2.0,173.0,3.0,0
48.0,1.0,4.0,0.0,2.0,166.0,0.0,3
56.0,0.0,4.0,0.0,2.0,150.0,2.0,2
42.0,1.0,1.0,0.0,2.0,178.0,2.0,0
59.0,1.0,1.0,0.0,2.0,145.0,0.0,0
60.0,0.0,4.0,0.0,2.0,161.0,0.0,1
63.0,0.0,2.0,0.0,0.0,179.0,2.0,0
42.0,1.0,3.0,1.0,0.0,194.0,0.0,0
66.0,1.0,2.0,0.0,0.0,120.0,3.0,2
54.0,1.0,2.0,0.0,2.0,195.0,1.0,1
69.0,1.0,3.0,0.0,2.0,146.0,3.0,2
50.0,1.0,3.0,0.0,0.0,163.0,0.0,0
51.0,1.0,4.0,0.0,0.0,122.0,3.0,3
62.0,0.0,4.0,1.0,0.0,106.0,3.0,2
68.0,0.0,3.0,0.0,2.0,115.0,0.0,0
67.0,1.0,4.0,0.0,2.0,125.0,2.0,3
69.0,1.0,1.0,1.0,2.0,131.0,1.0,0
45.0,0.0,4.0,0.0,2.0,152.0,0.0,0
50.0,0.0,2.0,0.0,0.0,162.0,0.0,0
59.0,1.0,1.0,0.0,2.0,125.0,0.0,1
50.0,0.0,4.0,0.0,2.0,159.0,0.0,0
64.0,0.0,4.0,0.0,0.0,154.0,0.0,0
57.0,1.0,3.0,1.0,0.0,173.0,1.0,0
64.0,0.0,3.0,0.0,0.0,133.0,0.0,0
43.0,1.0,4.0,0.0,0.0,161.0,0.0,0
45.0,1.0,4.0,0.0,2.0,147.0,3.0,3
58.0,1.0,4.0,0.0,2.0,130.0,2.0,3
50.0,1.0,4.0,0.0,2.0,126.0,0.0,3
55.0,1.0,2.0,0.0,0.0,155.0,0.0,0
62.0,0.0,4.0,0.0,0.0,154.0,0.0,1
37.0,0.0,3.0,0.0,0.0,170.0,0.0,0
38.0,1.0,1.0,0.0,0.0,182.0,0.0,4
41.0,1.0,3.0,0.0,2.0,168.0,0.0,0
66.0,0.0,4.0,1.0,0.0,165.0,2.0,3
52.0,1.0,4.0,0.0,0.0,160.0,1.0,1
56.0,1.0,1.0,0.0,2.0,162.0,0.0,0
46.0,0.0,2.0,0.0,0.0,172.0,0.0,0
46.0,0.0,4.0,0.0,2.0,152.0,0.0,0
64.0,0.0,4.0,0.0,0.0,122.0,2.0,0
59.0,1.0,4.0,0.0,2.0,182.0,0.0,0
41.0,0.0,3.0,0.0,2.0,172.0,0.0,0
54.0,0.0,3.0,0.0,2.0,167.0,0.0,0
39.0,0.0,3.0,0.0,0.0,179.0,0.0,0
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66.0,1.0,4.0,0.0,2.0,138.0,0.0,0
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71.0,0.0,4.0,0.0,0.0,125.0,0.0,0
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58.0,1.0,4.0,0.0,1.0,140.0,3.0,4
58.0,0.0,4.0,1.0,2.0,146.0,2.0,2
56.0,1.0,2.0,0.0,2.0,163.0,0.0,0
56.0,1.0,2.0,0.0,0.0,169.0,0.0,0
67.0,1.0,3.0,0.0,2.0,150.0,0.0,1
55.0,0.0,2.0,0.0,0.0,166.0,0.0,0
44.0,1.0,4.0,0.0,0.0,144.0,0.0,2
63.0,1.0,4.0,0.0,2.0,144.0,2.0,2
63.0,0.0,4.0,0.0,0.0,136.0,0.0,1
41.0,1.0,2.0,0.0,0.0,182.0,0.0,0
59.0,1.0,4.0,1.0,2.0,90.0,2.0,3
57.0,0.0,4.0,0.0,0.0,123.0,0.0,1
45.0,1.0,1.0,0.0,0.0,132.0,0.0,1
68.0,1.0,4.0,1.0,0.0,141.0,2.0,2
57.0,1.0,4.0,0.0,0.0,115.0,1.0,3
57.0,0.0,2.0,0.0,2.0,174.0,1.0,1
66.0,0.0,3.0,0.0,2.0,152.0,1.0,0
39.0,0.0,3.0,0.0,0.0,152.0,0.0,0
57.0,1.0,2.0,0.0,2.0,164.0,1.0,1
58.0,0.0,4.0,0.0,0.0,131.0,0.0,0
57.0,1.0,4.0,0.0,0.0,143.0,1.0,2
47.0,1.0,3.0,0.0,0.0,179.0,0.0,0
55.0,0.0,4.0,0.0,1.0,130.0,1.0,3
35.0,1.0,2.0,0.0,0.0,174.0,0.0,0
61.0,1.0,4.0,0.0,0.0,161.0,1.0,2
58.0,1.0,4.0,0.0,1.0,140.0,3.0,4
46.0,1.0,4.0,0.0,0.0,120.0,2.0,2
71.0,0.0,4.0,0.0,0.0,125.0,0.0,0
59.0,1.0,1.0,0.0,0.0,162.0,2.0,1
64.0,1.0,1.0,0.0,2.0,155.0,0.0,0
66.0,0.0,3.0,0.0,2.0,152.0,1.0,0
39.0,0.0,3.0,0.0,0.0,152.0,0.0,0
57.0,1.0,2.0,0.0,2.0,164.0,1.0,1
58.0,0.0,4.0,0.0,0.0,131.0,0.0,0
57.0,1.0,4.0,0.0,0.0,143.0,1.0,2
47.0,1.0,3.0,0.0,0.0,179.0,0.0,0
55.0,0.0,4.0,0.0,1.0,130.0,1.0,3
35.0,1.0,2.0,0.0,0.0,174.0,0.0,0
61.0,1.0,4.0,0.0,0.0,161.0,1.0,2
58.0,1.0,4.0,0.0,1.0,140.0,3.0,4
58.0,0.0,4.0,1.0,2.0,146.0,2.0,2
39.0,0.0,3.0,0.0,0.0,179.0,0.0,0
53.0,1.0,4.0,0.0,0.0,95.0,2.0,3
63.0,0.0,4.0,0.0,0.0,169.0,2.0,1
34.0,0.0,2.0,0.0,0.0,192.0,0.0,0
47.0,1.0,4.0,0.0,0.0,143.0,0.0,0
67.0,0.0,3.0,0.0,0.0,172.0,1.0,0
54.0,1.0,4.0,0.0,2.0,108.0,1.0,3
66.0,1.0,4.0,0.0,2.0,132.0,1.0,2
52.0,0.0,3.0,0.0,2.0,169.0,0.0,0
55.0,0.0,4.0,0.0,1.0,117.0,0.0,2
49.0,1.0,3.0,0.0,2.0,126.0,3.0,1
74.0,0.0,2.0,0.0,2.0,121.0,1.0,0
54.0,0.0,3.0,0.0,0.0,163.0,1.0,0
54.0,1.0,4.0,0.0,2.0,116.0,2.0,3
56.0,1.0,4.0,1.0,2.0,103.0,0.0,2
46.0,1.0,4.0,0.0,2.0,144.0,0.0,1
49.0,0.0,2.0,0.0,0.0,162.0,0.0,0
42.0,1.0,2.0,0.0,0.0,162.0,0.0,0
45.0,1.0,4.0,0.0,2.0,185.0,0.0,0
34.0,1.0,1.0,0.0,2.0,174.0,0.0,0
57.0,0.0,4.0,0.0,2.0,159.0,1.0,0
71.0,0.0,3.0,1.0,2.0,130.0,1.0,0
49.0,1.0,3.0,0.0,0.0,139.0,3.0,3
54.0,1.0,2.0,0.0,0.0,156.0,0.0,0
59.0,1.0,4.0,0.0,0.0,162.0,1.0,2
57.0,1.0,3.0,0.0,2.0,150.0,1.0,1
61.0,1.0,4.0,0.0,0.0,140.0,1.0,2
39.0,1.0,4.0,0.0,0.0,140.0,0.0,3
61.0,0.0,4.0,0.0,2.0,146.0,0.0,1
56.0,1.0,4.0,1.0,2.0,144.0,1.0,1
52.0,1.0,1.0,0.0,2.0,190.0,0.0,0
43.0,0.0,4.0,1.0,2.0,136.0,0.0,2
67.0,1.0,4.0,0.0,2.0,108.0,3.0,2
67.0,1.0,4.0,0.0,2.0,129.0,2.0,1
37.0,1.0,3.0,0.0,0.0,187.0,0.0,0
41.0,0.0,2.0,0.0,2.0,172.0,0.0,0
56.0,1.0,2.0,0.0,0.0,178.0,0.0,0
62.0,0.0,4.0,0.0,2.0,160.0,2.0,3
57.0,0.0,4.0,0.0,0.0,163.0,0.0,0
63.0,1.0,4.0,0.0,2.0,147.0,1.0,2
53.0,1.0,4.0,1.0,2.0,155.0,0.0,1
57.0,1.0,4.0,0.0,0.0,148.0,0.0,0
56.0,0.0,2.0,0.0,2.0,153.0,0.0,0
56.0,1.0,3.0,1.0,2.0,142.0,1.0,2
44.0,1.0,2.0,0.0,0.0,173.0,0.0,0
52.0,1.0,3.0,1.0,0.0,162.0,0.0,0
57.0,1.0,3.0,0.0,0.0,174.0,0.0,0
48.0,1.0,2.0,0.0,0.0,168.0,0.0,1
54.0,1.0,4.0,0.0,0.0,160.0,0.0,0
48.0,0.0,3.0,0.0,0.0,139.0,0.0,0
50.0,1.0,2.0,0.0,0.0,160.0,0.0,0
54.0,0.0,2.0,0.0,0.0,150.0,1.0,0
54.0,0.0,2.0,0.0,1.0,155.0,1.0,0
54.0,0.0,2.0,0.0,0.0,130.0,1.0,0
54.0,0.0,2.0,0.0,0.0,130.0,1.0,0
54.0,1.0,1.0,0.0,0.0,137.0,1.0,0
54.0,1.0,2.0,0.0,0.0,142.0,1.0,0
54.0,1.0,2.0,0.0,0.0,154.0,1.0,0
54.0,1.0,2.0,0.0,0.0,110.0,1.0,0
54.0,1.0,2.0,0.0,1.0,130.0,1.0,0
59.0,1.0,4.0,0.0,0.0,140.0,0.0,0
47.0,1.0,4.0,0.0,0.0,98.0,0.0,1
56.0,1.0,4.0,0.0,0.0,120.0,0.0,1
59.0,1.0,4.0,0.0,1.0,131.0,0.0,0
CodePudding user response：
You are training your model with normalized data but predicting on data that is not normalized. X_test is normalized data so the predictions on it are as expected. dummyTest is not normalized. If you normalize the dummyTest variable before feeding it to your neural network like so:
dummyTest[0] -= mean
dummyTest[0] /= std
You will receive the expected output(1)