def check_model_1(word):
model=load_model("model2.h5")
crs=[]
d={}
d=verify_enc.initializare_big()
test = []
encoding = verify_enc.verify_encoding(word,d)
litere_mari = verify_enc.count_litere_mari(word)
litere_mici = verify_enc.count_litere_mici(word)
litere_peste_f = verify_enc.count_litere_mai_mari_de_f(word)
count_cifre = verify_enc.count_cifre(word)
lungime = verify_enc.get_len(word)
test.append(float(encoding))
test.append(float(litere_mari))
test.append(float(litere_mici))
test.append(float(litere_peste_f))
test.append(float(lungime))
#test.append(float(count_cifre))
crs.append(test)
X_train=np.array(crs)
# =============================================================================
# scaler = StandardScaler()
# test_scaled=scaler.fit(X_train)
# =============================================================================
out = model.predict(crs)
print (int(out[0][0]))
def check_model_2(word):
model=load_model("model3.h5")
crs=[]
d={}
d=verify_enc.initializare(d)
test = []
encoding = verify_enc.verify_encoding(word,d)
test.append(float(encoding))
#test.append(float(count_cifre))
crs.append(test)
X_train=np.array(crs)
# =============================================================================
# scaler = StandardScaler()
# test_scaled=scaler.fit(X_train)
# =============================================================================
out = model.predict(crs)
print (int(out[0][0]))
print (encoding)
def check_model_3(word):
model = load_model("./modules/MachineLearning/models/model4.5.h5")
crs = []
d = {}
d = verify_enc.initializare_extins()
test = []
encoding = verify_enc.verify_encoding(word, d)
if (encoding == None):
encoding = 0
litere_mari = verify_enc.count_litere_mari(word)
litere_mici = verify_enc.count_litere_mici(word)
litere_peste_f = verify_enc.count_litere_mai_mari_de_f(word)
lungime = verify_enc.get_len(word)
cifre = verify_enc.count_cifre(word)
puncte = verify_enc.count_dots(word)
minus = verify_enc.count_lines(word)
underscore = verify_enc.count_underscore(word)
slash = verify_enc.count_slashes(word)
entropy = verify_enc.get_entropy(word)
all_lens = verify_enc.get_every_len(word)
test.append(float(encoding))
test.append(float(litere_mari))
test.append(float(litere_mici))
test.append(float(litere_peste_f))
test.append(float(lungime))
test.append(float(cifre))
test.append(float(puncte))
test.append(float(minus))
test.append(float(underscore))
test.append(float(slash))
test.append(float(entropy))
for z in all_lens:
test.append(float(z))
crs.append(test)
X_train = np.array(crs)
# =============================================================================
# scaler = StandardScaler()
# test_scaled=scaler.fit(X_train)
# =============================================================================
out = model.predict(crs)
return int(out[0][0])
model2.add(layers.Dense(units=1, activation="sigmoid"))
X2_train_scaled, Y2_train=shuffle(X2_train_scaled, Y2_train)
model2.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
model2.fit(X2_train_scaled, Y2_train, validation_split=0.1, epochs=5)
model2.save("model3.h5")
print ("Am terminat cu al doilea model")
'''
true_ads = verify_enc.read_file('true_extins.csv')
false_ads = verify_enc.read_file('false_extins.csv')
d = {}
d = verify_enc.initializare_extins()
for i in true_ads:
encoding = verify_enc.verify_encoding(i[0], d)
litere_mari = verify_enc.count_litere_mari(i[0])
litere_mici = verify_enc.count_litere_mici(i[0])
litere_peste_f = verify_enc.count_litere_mai_mari_de_f(i[0])
lungime = verify_enc.get_len(i[0])
cifre = verify_enc.count_cifre(i[0])
puncte = verify_enc.count_dots(i[0])
minus = verify_enc.count_lines(i[0])
underscore = verify_enc.count_underscore(i[0])
slash = verify_enc.count_slashes(i[0])
entropy = verify_enc.get_entropy(i[0])
all_lens = verify_enc.get_every_len(i[0])
array = []
if (encoding == None):
encoding = 0
array.append(float(encoding))