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]])
Select the correct answer from above options
