def decode_batch(test_func, word_batch):
out = test_func([word_batch])[0]
ret = []
for j in range(out.shape[0]):
out_best = list(np.argmax(out[j, 2:], 1))
out_best = [k for k, g in itertools.groupby(out_best)]
outstr = labels_to_text(out_best)
ret.append(outstr)
return ret
def predict(model: Sequential, tokenizer: Tokenizer, word: str, seq_len: int = 4):
encoded_text = r .texts_to_sequences([word])[0]
pad_encoded = pad_sequences([encoded_text], maxlen=seq_len, truncating='pre')
print(encoded_text, pad_encoded)
pred = model.predict(pad_encoded)[0]
for _ in range(0, 3):
index = np.argmax(pred, axis=0)
pred_word = tokenizer.index_word[index]
print("Next word suggestion:", pred_word)
pred = np.delete(pred, index)
#Εκπαίδευση και αξιολόγηση του μοντέλου
batch_size = 32
model_lstm.fit(X1_train,Y1_train,epochs = 25,batch_size=batch_size, verbose = 2, validation_data=(X1_test,Y1_test))
y_pred = model_lstm.predict(X1_test)
loss,acc = model_lstm.evaluate(X1_test, Y1_test, verbose = 1, batch_size = batch_size)
print('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))
pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0
for x in range(len(X1_test)):
result = model_lstm.predict(X1_test[x].reshape(1, X1_test.shape[1]), batch_size=1, verbose=2)[0]
if np.argmax(result) == np.argmax(Y1_test[x]):
if np.argmax(Y1_test[x]) == 0:
neg_correct += 1
else:
pos_correct += 1
if np.argmax(Y1_test[x]) == 0:
neg_cnt += 1
else:
pos_cnt += 1
print("Ακρίβεια Αποδεκτών Γνωμοδοτήσεων", pos_correct / pos_cnt * 100, "%")
print("Ακρίβεια Γνωμοδοτησεων σε εκρεμμότητα", neg_correct / neg_cnt * 100, "%")
from keras.models import model_from_json
from pathlib import Path
from keras.preprocessing import image
from pandas import np
classes = [
"Plane", "Car", "Bird", "Cat", "Deer", "Dog", "Frog", "Horse", "Boat",
"Truck"
]
f = Path("model_structure.json")
model_structure = f.read_text()
model = model_from_json(model_structure)
model.load_weights("model_weights.h5")
img = image.load_img("tractor-3-1386656.jpg", target_size=(32, 32))
img_arry = image.img_to_array(img) / 255
list_img = np.expand_dims(img_arry, axis=0)
res = model.predict(list_img)
single = res[0]
most_likely = int(np.argmax(single))
class_like = single[most_likely]
class_name = classes[most_likely]
print("The image is {} with accuracy: {:2f}".format(class_name, class_like))
def _class_maps_to_colored_maps(class_map: np.ndarray) -> np.ndarray:
return np.argmax(class_map, axis=3)
cv2.namedWindow('window_frame')
while True:
_, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_detection.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(gray, (x - int(0.2 * w), y - int(0.3 * h)), (x + int(1.2 * w), y + int(1.2 * h)),
(255, 0, 0), 2)
face = gray[y - int(0.3 * h): y + int(1.2 * h), x - int(0.2 * w): x + int(1.2 * w)]
try:
face = cv2.resize(face, (64, 64))
except:
continue
face = np.expand_dims(face, 0)
face = np.expand_dims(face, -1)
gender_arg = np.argmax(gender_classifier.predict(face))
gender = gender_labels[gender_arg]
gender_window.append(gender)
if len(gender_window) >= frame_window:
gender_window.pop(0)
try:
gender_mode = mode(gender_window)
except:
continue
cv2.putText(gray, gender_mode, (x, y - 30), font, .7, (255, 0, 0), 1, cv2.LINE_AA)
try:
cv2.imshow('window_frame', gray)
except:
continue
from sklearn.metrics import accuracy_score, confusion_matrix
model = Sequential()
model.add(Embedding(vocab, 50, input_length=sentence_length))
model.add(Dropout(0.2))
model.add(LSTM(100, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(50))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
x_train, x_test, y_train, y_test = train_test_split(inp_fin,
out_fin,
test_size=0.2,
random_state=42)
model.summary()
history = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=64,
epochs=10)
prediction = np.argmax(model.predict(x_test), axis=-1)
print(accuracy_score(y_test, prediction))
model.save('model.h5')
