Trying to make classifier for diseases, but it returns wrong output

Question

I am trying to make an app that can get the patient's symptoms as inputs and outputs the three most likely diseases.

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.4)

inputs = keras.Input(shape=(9,))

hidden_1 = keras.layers.Dense(12, activation='selu')(inputs)

hidden_2 = keras.layers.Dense(12, activation='relu')(hidden_1)

outputs = keras.layers.Dense(32, activation='sigmoid')(hidden_2)

model = keras.Model(inputs=inputs, outputs=outputs)

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['categorical_accuracy'])

model.summary()

model.fit(x_train, y_train, epochs=200) # starts training

prediction = model.predict(x_test)

print(prediction)

scores = model.evaluate(x_test, y_test, verbose=0)

print(scores)

print(prediction[0])

print(y_test[0])

model.save("modeldisease.h5")

but when i save and load this model into another python file, the outputs list looks like this:

  [[0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00

  1.000000e+00 0.000000e+00 0.000000e+00 1.842211e-21 0.000000e+00

  0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00

  1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 0.000000e+00

  0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00

  0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00

  1.000000e+00 0.000000e+00]]

100.0 100.0 100.0

HEPATITE A HEPATITE_ALCOOLICA REFLUXO_GASTROESOFAGICO

Process finished with exit code 0

What am I doing wrong? shouldn't it just return me this prediction list with all the values adding up to 1? 

full code: `

from tensorflow import keras

import numpy as np

import sklearn

from sklearn import preprocessing

import pandas as pd

data = pd.read_csv("DATA.csv", sep=";")

obj_data = data.select_dtypes(include=["object"]).copy()

obj_data_names = []

for col in obj_data.columns:

    obj_data_names.append(col)

for col_name in obj_data_names:

    data[col_name] = data[col_name].astype('category')

    data[col_name + "_cat"] = data[col_name].cat.codes

    data[col_name] = data[col_name + "_cat"]

    data.drop(col_name + "_cat", 1, inplace=True)

print(data)

data.fillna(0, inplace=True)

x = np.array(data.drop(["Doenca"], 1))

y = np.array(data["Doenca"])

x = preprocessing.normalize(x)

y = keras.utils.to_categorical(y)

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.4)

inputs = keras.Input(shape=(9,))

hidden_1 = keras.layers.Dense(12, activation='selu')(inputs)

hidden_2 = keras.layers.Dense(12, activation='relu')(hidden_1)

outputs = keras.layers.Dense(32, activation='sigmoid')(hidden_2)

model = keras.Model(inputs=inputs, outputs=outputs)

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['categorical_accuracy'])

model.summary()

model.fit(x_train, y_train, epochs=200) # starts training

prediction = model.predict(x_test)

print(prediction)

scores = model.evaluate(x_test, y_test, verbose=0)

print(scores)

print(prediction[0])

print(y_test[0])

model.save("modeldisease.h5")

for i in range(len(y_test)):

    max_expected = np.amax(y_test[i])

    #print(max_expected)

    y_expected = np.where(y_test[i] == max_expected)

    print(y_expected[0])

    max_predicted = np.amax(prediction[i])

    #print(max_predicted)

    y_predicted = np.where(prediction[i] == max_predicted)

    print(y_predicted[0])

    classes = ["ACNE", "AIDS", "ALERGIA", "ARTRITE", "ARTROSE", "CATAPORA", "COLESTASE", "DENGUE", "DIABETES", "ENXAQUECA", "ESPONDILOSE",

               "FEBRE_TIFOIDE", "GASTROENTERITE", "GRIPE", "HEPATITE_ALCOOLICA", "HEPATITE A", "HEPATITE B", "HEPATITE C", '"HEPATITE D',

               "HEPATITE E", "HIPERTENSAO", "HIPERTENSAO", "HIPERTIROIDISMO", "HIPOGLICEMIA", "HIPOTIREODISMO", "IMPETIGO", "INFECCA_URINA",

               "MALARIA", "PNEUMONIA", "PSORIASE", "REFLUXO_GASTROESOFAGICO", 'TUBERCULOSE', "ULCERA GASTRICA"]

    print(f"Valor esperado: {classes[int(y_expected[0])]}, Valor previsto: {classes[int(y_predicted[0])]}")

# print(prediction[0][0])

# print(round(prediction[0][0]))

# print(classes[round(int(prediction[0][0]))])

#print(y_test)

#print(prediction)

load code:

from tensorflow import keras

model = keras.models.load_model("modeldisease.h5")

result = model.predict([(12, 40, 39, 17, 0, 0, 0, 0, 0)])

print(result)

sortedshit = result[0].argsort()[-3:][::-1]

p1 = result[0][sortedshit[0]]

p2 = result[0][sortedshit[1]]

p3 = result[0][sortedshit[2]]

classes = ["ACNE", "AIDS", "ALERGIA", "ARTRITE", "ARTROSE", "CATAPORA", "COLESTASE", "DENGUE", "DIABETES", "ENXAQUECA",

           "ESPONDILOSE",

           "FEBRE_TIFOIDE", "GASTROENTERITE", "GRIPE", "HEPATITE_ALCOOLICA", "HEPATITE A", "HEPATITE B", "HEPATITE C",

           '"HEPATITE D',

           "HEPATITE E", "HIPERTENSAO", "HIPERTENSAO", "HIPERTIROIDISMO", "HIPOGLICEMIA", "HIPOTIREODISMO", "IMPETIGO",

           "INFECCA_URINA",

           "MALARIA", "PNEUMONIA", "PSORIASE", "REFLUXO_GASTROESOFAGICO", 'TUBERCULOSE', "ULCERA GASTRICA"]

print(p1*100, p2*100, p3*100)

print(classes[sortedshit[0]], classes[sortedshit[1]], classes[sortedshit[2]])

Answer 1

Apply softmax activation function instead of sigmoid:

outputs = keras.Dense(32,activation='softmax')(hidden_2)

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