コード例 #1
0
def predict():
    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test, imgs_id_test = load_test_data()

    imgs_test = imgs_test.astype('float32')

    mean = np.mean(imgs_test)  # mean for data centering
    std = np.std(imgs_test)  # std for data normalization

    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model = build_model()
    model.load_weights('model.hdf5')

    print('-' * 30)
    print('Predicting masks on test data...')
    print('-' * 30)

    imgs_testing_train = model.predict(imgs_test, verbose=1)
    np.save('imgs_testing_train1.npy', imgs_testing_train)
コード例 #2
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def submission():
    from data import load_test_data
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = np.load('imgs_mask_test_nfold.npy')

    argsort = np.argsort(imgs_id_test)
    imgs_id_test = imgs_id_test[argsort]
    imgs_test = imgs_test[argsort]

    total = imgs_test.shape[0]
    ids = []
    rles = []
    for i in range(total):
        img = imgs_test[i, 0]
        img = prep(img)
        rle = run_length_enc(img)

        rles.append(rle)
        ids.append(imgs_id_test[i])

        if i % 100 == 0:
            print('{}/{}'.format(i, total))

    first_row = 'img,pixels'
    file_name = 'submission.csv'

    with open(file_name, 'w+') as f:
        f.write(first_row + '\n')
        for i in range(total):
            s = str(ids[i]) + ',' + rles[i]
            f.write(s + '\n')
コード例 #3
0
ファイル: train.py プロジェクト: DaMaillard/NervousBreakDown
def predict_unet(data_path, save_path, basename="", weight_load = "", train_mean=0, train_std=1):
    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data(data_path)
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= train_mean
    imgs_test /= train_std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights(weight_load)

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    mask_filename = save_path + 'imgs_mask_test' + basename + '.npy'
    np.save(mask_filename, imgs_mask_test)
    
    submission_filename = save_path + '/submission' + basename + '.csv'
    submission(data_path, submission_filename, mask_filename, .6)
コード例 #4
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def check_data():

    imgs_test, imgs_id_test = load_test_data()
    imgs_mask_test = np.load('imgs_mask_test.npy')
    print('-' * 30)
    print('Saving predicted masks to files...')
    print('-' * 30)
    pred_dir = 'preds/'
    if not os.path.exists(pred_dir):
        os.mkdir(pred_dir)

    print(imgs_mask_test.shape)
    print(imgs_id_test.shape)

    for image, image_id in zip(imgs_mask_test, imgs_id_test):

        # print(image.shape)
        image = (image[:, :, 0] * 255.).astype(np.uint8)

        # print(image.shape)
        #cv2.imshow('1', image)
        #if cv2.waitKey(0) == 27:
        #    exit( 1 )

        imsave('preds/{}_pred.png'.format(image_id), image)
コード例 #5
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def load_trained_model():
    model = get_unet()
    model.load_weights(weights_path)

    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    mean = np.mean(imgs_test)
    std = np.std(imgs_test)  # std for data normalization

    imgs_test -= mean
    imgs_test /= std

    imgs_mask_test = model.predict(imgs_test, verbose=1, batch_size=1)

    np.save('imgs_mask_test.npy', imgs_mask_test)
    pred_dir = 'preds'
    if not os.path.exists(pred_dir):
        os.mkdir(pred_dir)
    for image, image_id in zip(imgs_mask_test, imgs_id_test):
        image = (image[:, :, 0] * 255.).astype(np.uint8)
        image_resized = resize(image, (1040, 1392), mode='constant')
        imsave(os.path.join(pred_dir,
                            str(image_id) + '_pred.png'), image_resized)
コード例 #6
0
ファイル: train.py プロジェクト: amovschin/NervousBreakDown
def load_data(data_path):
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train, imgs_patient_train = load_train_data(data_path)

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]
    
    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data(data_path)
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std
    
    return imgs_train, imgs_mask_train, imgs_patient_train, imgs_test
コード例 #7
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def test():
    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    # imgs_train, imgs_mask_train = load_train_data()

    # imgs_train = imgs_train.astype('float32')
    # mean = np.mean(imgs_train)  # mean for data centering
    #std = np.std(imgs_train)  # std for data normalization

    imgs_test = load_test_data()
    mean = np.load('scaler.npz')['mean']
    std = np.load('scaler.npz')['std']
    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Creating and compiling model...')
    print('-' * 30)
    model = get_unet()
    # print('Loading saved weights...')
    # print('-'*30)
    model.load_weights('final_weights.h5')

    # print('-'*30)
    print('Predicting masks on test data...')
    # print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=2)
    np.savez_compressed('/imgs_test_mask', imgs_test_mask=imgs_mask_test)
    print('Saving predicted masks to files...')
コード例 #8
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def load_model_network():

    X_test, Y_test = load_test_data()

    model = CNN('digitos.hdf5')
    optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
    model.compile(optimizer=optimizer,
                  loss="categorical_crossentropy",
                  metrics=["accuracy"])

    Y_pred = model.predict(X_test)

    # Convert predictions classes to one hot vectors
    Y_pred_classes = np.argmax(Y_pred, axis=1)

    # Convert validation observations to one hot vectors
    Y_true = np.argmax(Y_test, axis=1)

    # compute the confusion matrix
    confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)

    print(" ACURÀCIA = %.2f" % (accuracy_score(Y_true, Y_pred_classes) * 100),
          "%")

    # plot the confusion matrix
    plot_confusion_matrix(confusion_mtx, classes=range(10))
コード例 #9
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def length_analyze():
    # load data
    #get_data = lambda d: [d["1"], d["2"], d["3"]] # character
    #get_data = lambda d: [d["1_tokenized"], d["2_tokenized"], d["3_tokenized"]] # word
    get_data = lambda d: [d["1_m_2000"], d["2_m_2000"], d["3_m_2000"]] # word
    
    data = load_data("train.txt")
    x_train = get_data(data)

    data = load_data("dev.txt")
    x_valid = get_data(data)

    data = load_test_data("test.txt")
    x_test = get_data(data)

    l_train = np.array([len(row) for x in x_train for row in x])
    l_valid = np.array([len(row) for x in x_valid for row in x])
    l_test = np.array([len(row) for x in x_test for row in x])

    for length, name in zip([l_train, l_valid, l_test], ["train", "valid", "test"]):
        print("============================")
        print("length max", np.max(length))
        print("length min", np.min(length))
        print("length mean", length.mean())
        print("length std", length.std())
        print("length median", np.median(length))
        print("length percentile", np.percentile(length, 75))
コード例 #10
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def submission():
    from data import load_test_data
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = np.load('imgs_mask_test.npy')

    argsort = np.argsort(imgs_id_test)
    imgs_id_test = imgs_id_test[argsort]
    imgs_test = imgs_test[argsort]

    total = imgs_test.shape[0]
    ids = []
    rles = []
    for i in range(total):
        img = imgs_test[i, 0]
        img = prep(img)
        rle = run_length_enc(img)

        rles.append(rle)
        ids.append(imgs_id_test[i])

        if i % 100 == 0:
            print('{}/{}'.format(i, total))

    first_row = 'img,pixels'
    file_name = 'submission.csv'

    with open(file_name, 'w+') as f:
        f.write(first_row + '\n')
        for i in range(total):
            s = str(ids[i]) + ',' + rles[i]
            f.write(s + '\n')
コード例 #11
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train, imgs_mask_train = preprocess(imgs_train, imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-' * 30)
    print('Creating and compiling model...')
    print('-' * 30)
    model = get_unet()
    model_checkpoint = ModelCheckpoint('unet.hdf5',
                                       monitor='val_loss',
                                       save_best_only=True)
    plot(model, to_file='model.png')

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
    model.fit(imgs_train,
              imgs_mask_train,
              batch_size=1,
              nb_epoch=500,
              verbose=1,
              shuffle=True,
              callbacks=[model_checkpoint, early_stopping],
              validation_split=0.10)

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model.load_weights('unet.hdf5')

    print('-' * 30)
    print('Predicting masks on test data...')
    print('-' * 30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #12
0
 def test_dataloader(self):
     # Load data and create a DataLoader
     X_test, y_test = load_test_data()
     test_dataloader = to_dataloader(X_test,
                                     y_test,
                                     batch_size=cfg.BATCH_SIZE)
     return test_dataloader
コード例 #13
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-' * 30)
    print('Creating and compiling model...')
    print('-' * 30)
    model = get_unet()
    # model.save_weights('/unet.hdf5')
    model_checkpoint = ModelCheckpoint(filepath='unet.hdf5',
                                       monitor='loss',
                                       save_best_only=True)

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    model.fit(imgs_train,
              imgs_mask_train,
              batch_size=32,
              nb_epoch=nb_epoch,
              verbose=1,
              shuffle=True,
              callbacks=[model_checkpoint])

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    #imgs_test, imgs_id_test = load_test_data()
    imgs_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model.load_weights('unet.hdf5')

    print('-' * 30)
    print('Predicting masks on test data...')
    print('-' * 30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #14
0
def word_distribution():
    # load data
    #get_data = lambda d: [d["1"], d["2"], d["3"]] # character
    #get_data = lambda d: [d["1_tokenized"], d["2_tokenized"], d["3_tokenized"]] # word
    get_data = lambda d: [d["1_m_2000"], d["2_m_2000"], d["3_m_2000"]] # word
    
    data = load_data("train.txt")
    x_train = get_data(data)

    data = load_data("dev.txt")
    x_valid = get_data(data)

    data = load_test_data("test.txt")
    x_test = get_data(data)

    # freq
    freq_train = Counter(
        w
        for x in x_train
        for row in x
        for w in row
    )
    freq_valid = Counter(
        w
        for x in x_valid
        for row in x
        for w in row
    )
    freq_test = Counter(
        w
        for x in x_test
        for row in x
        for w in row
    )

    # statistic
    print("Train All", sum(freq_train.values()))
    print("Valid All", sum(freq_valid.values()))

    # overlap
    print("=====================================")
    for min_freq in [0, 5, 10]:
        print("min_freq = ", min_freq)
        available_chars = {k:v for k, v in freq_train.items() if v >= min_freq} 
        not_in_valid = {k:v for k, v in freq_valid.items() if k not in available_chars}
        not_in_test  = {k:v for k, v in freq_test.items() if k not in available_chars}
        
        print("Train # unique chars = {}, # chars = {}".format(len(available_chars), sum(available_chars.values())))
        print("Valid Not in Train -- # unique chars = {}, # chars = {} | # available chars = {}".format(
            len(not_in_valid), 
            sum(not_in_valid.values()), 
            sum(freq_valid.values()) - sum(not_in_valid.values()), 
        ))
        print("Test Not in Train -- # unique chars = {}, # chars = {} | # available chars = {}".format(
            len(not_in_test), 
            sum(not_in_test.values()), 
            sum(freq_test.values()) - sum(not_in_test.values()), 
        ))
        print()
コード例 #15
0
 def val_dataloader(self):
     # Load data and create a DataLoader
     X_val, y_val = load_test_data()
     val_dataloader = to_dataloader(X_val,
                                    y_val[:, :, :, 0:self.output_shape[2]],
                                    batch_size=cfg.BATCH_SIZE,
                                    shuffle=False)
     return val_dataloader
コード例 #16
0
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()
    plot(model, to_file='model.png', show_shapes=True)
    model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    datagen = ImageDataGenerator(
            rotation_range=5,
            vertical_flip=True,
            horizontal_flip=True,
            )
    model.fit_generator(datagen.flow(imgs_train, imgs_mask_train, batch_size=32, shuffle=True),
            samples_per_epoch=len(imgs_train), nb_epoch=120, verbose=1, callbacks=[model_checkpoint])


    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #17
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def predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train, imgs_label_train = load_train_data()
    # imgs_train = imgs_train[:32]
    # imgs_label_train = imgs_label_train[:32]

    imgs_train = preprocess(imgs_train)
    imgs_label_train = preprocess(imgs_label_train)
    imgs_label_train = np_utils.to_categorical(imgs_label_train)
    imgs_label_train = imgs_label_train.reshape((280,96,96,4))

    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_mask_test, imgs_label_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)
    imgs_label_test = preprocess(imgs_label_test)
    imgs_label_test = np_utils.to_categorical(imgs_label_test)
    imgs_label_test = imgs_label_test.reshape((70,96,96,4))

    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)

    model = get_unet()
    # model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss', save_best_only=True)

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('weights.h5')

    print('-'*30)
    print('Predicting labels on test data...')
    print('-'*30)
    predicted = model.predict(imgs_test, verbose=1)
    np.save('predicted.npy', predicted)

    print('-' * 30)
    print('Saving predicted labels to files...')
    print('-' * 30)
    pred_dir = 'preds'
    if not os.path.exists(pred_dir):
        os.mkdir(pred_dir)
    for image, image_id in zip(predicted.argmax(axis=3)[...,np.newaxis], imgs_id_test):
        image = (image[:, :, 0]).astype(np.uint8)
        scipy.misc.imsave(os.path.join(pred_dir, str(image_id) + '_pred.png'), image)
コード例 #18
0
def load_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    ## Image Resizing
    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    ## Network Preprocessing 
    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization
    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()

    print('-'*30)
    print('Loading and preprocessing test data... NOT LOADING LABELS')
    print('-'*30)
    imgs_test, imgs_id_test , _ = load_test_data()

    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('weights.h5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test_PRED.npy', imgs_mask_test)

    print('-' * 30)
    print('Saving predicted masks to files...')
    print('-' * 30)
    pred_dir = 'preds'
    if not os.path.exists(pred_dir):
        os.mkdir(pred_dir)
    for image, image_id in zip(imgs_mask_test, imgs_id_test):
        image = (image[:, :, 0] * 255.).astype(np.uint8)
        imsave(os.path.join(pred_dir, image_id), image)
コード例 #19
0
def main(hps):
    
    #pre_train
    if args.pre_train:
        word2idx, idx2word, verb2idx, idx2verb = make_vocab(hps)
#         word2idx, idx2word, verb2idx, idx2verb = load_vocab(hps)
#         mapping, vectors = load_glove(hps)
#         weights_matrix = make_pre_trained_word_embedding(mapping, vectors, word2idx.keys(), hps)
        hps = hps._replace(vocab_size=len(word2idx))
        hps = hps._replace(verb_vocab_size=len(verb2idx))
        hps = hps._replace(pre_train=True)
        
        print('parameters:')
        print(hps)
        
        train_loader, valid_loader, char_weights, action_weights = load_train_data(word2idx, verb2idx, hps)
        model = Model(char_weights, action_weights, hps)
#         model.load_state_dict(torch.load(hps.test_path + 'models/pre_train.model'))
        model = model.cuda()
        optimizer = optim.Adam(model.parameters(), lr=hps.lr, weight_decay=hps.weight_decay)
        print('pre_training', flush=True)
        pre_train(model, optimizer, train_loader, valid_loader, idx2word, hps)
        
    #train
    elif args.train:
#         word2idx, idx2word = make_vocab(hps)
        word2idx, idx2word = load_vocab(hps)
        hps = hps._replace(vocab_size=len(word2idx))
        
            
        print('parameters:')
        print(hps)
        
        train_loader, valid_loader, char_weights, action_weights = load_train_data(word2idx, hps)
        model = Model(char_weights, action_weights, hps)
#         model.load_state_dict(torch.load(hps.test_path + 'models/best.model'))
        if args.reload:
            model.load_state_dict(torch.load(hps.test_path + hps.save_path.format(args.reload_epoch)))

        model.cuda()
#         model = nn.DataParallel(model,device_ids=[0])
        optimizer = optim.Adam(model.parameters(), lr=hps.lr, weight_decay=hps.weight_decay)
        print('training', flush=True)
        train(model, optimizer, train_loader, valid_loader, idx2word, hps)
    
    #test
    if args.test:
        print('testing', flush=True)
        word2idx, idx2word = load_vocab(hps)
        hps = hps._replace(vocab_size=len(word2idx))
        hps = hps._replace(test=True)
        model = Model([0] * hps.max_num_char, [0] * hps.vocab_size, hps)
        model.load_state_dict(torch.load(hps.test_path + hps.save_path))
        model.cuda()
        test_loader, anony2names = load_test_data(word2idx, hps)
        test(model, test_loader, idx2word, anony2names, hps)
コード例 #20
0
ファイル: train_yn.py プロジェクト: bmalthi/bnerveseg
def load_data():
    imgs_train, imgs_mask_train = load_train_data()
    imgs_train = imgs_train.astype('float32')
    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]
    print('We have ',imgs_train.shape[0],' training samples')
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = imgs_test.astype('float32')
    print('We have ',imgs_test.shape[0],' test samples')
    return imgs_train, imgs_mask_train, imgs_test, imgs_id_test
コード例 #21
0
def train_and_predict():
    
    print("-"*30)
    print("Loading train data...")
    print('-'*30)
    
    train_images,train_masks = load_train_data()
    
#   train_images = preprocess(train_images)
#   train_masks = preprocess(train_masks)
    
#    np.save("./generated_data/train_images_64*80.npy",train_images)
#    np.save("./generated_data/train_masks_64*80.npy",train_masks)


    
    train_images = train_images.astype('float32')
    mean  = np.mean(train_images)
    std = np.std(train_images)

    train_images -= mean
    train_images /= std

    train_masks = np.load("./generated_data/train_class.npy")

    print('-'*30)
    print('Creating and compile model...')
    print('-'*30)

    model = create_model()
    model_checkpoint = ModelCheckpoint('./generated_data/model_2nd_007.hdf5',monitor='loss',save_best_only=True)

    print('-'*30)
    print('fitting model...')
    print('-'*30)
    model.fit(train_images,train_masks,batch_size=120,nb_epoch=60,verbose=1,shuffle=True,callbacks=[model_checkpoint])
    
    print('-'*30)
    print('Loading test data...')
    print('-'*30)
    
    test_images,test_ids = load_test_data()
#    test_images = preprocess(test_images)
    
#    np.save("./generated_data/test_images_64*80.npy",test_images)
    
    test_images = test_images.astype('float32')
    test_images -= mean
    test_images /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    test_class = model.predict(test_images,verbose=1)
    np.save('./generated_data/test_class_full_model007.npy',test_class)
コード例 #22
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    imgs_train, imgs_train_label = load_train_data()

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    print('-' * 30)
    print('Creating and compiling model...')
    print('-' * 30)
    model = get_unet()
    model_checkpoint = ModelCheckpoint('unet.hdf5',
                                       monitor='loss',
                                       save_best_only=True)

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    model.fit(imgs_train,
              imgs_train_label,
              batch_size=32,
              nb_epoch=200,
              verbose=1,
              shuffle=True,
              callbacks=[model_checkpoint])

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test = load_test_data()

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model.load_weights('unet.hdf5')

    print('-' * 30)
    print('Predicting results on test data...')
    print('-' * 30)
    imgs_test_result = model.predict(imgs_test, verbose=1)
    np.save('imgs_test_result.npy', imgs_test_result)

    test_result = np.load('imgs_test_result.npy')
    np.savetxt("test_result.csv", test_result, delimiter=",")
コード例 #23
0
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    imgs_has_mask_train = (imgs_mask_train.reshape(imgs_mask_train.shape[0], -1).max(axis=1) > 0) * 1

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()
    model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)
    remote_monitor = RemoteMonitor(root='http://localhost:9000')

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    model.fit(imgs_train, [imgs_mask_train, imgs_has_mask_train], batch_size=32, nb_epoch=20, verbose=1, shuffle=True,
              callbacks=[model_checkpoint, remote_monitor])

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)[0]  # Get output only for first output
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #24
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    """
    imgs_train, imgs_mask_train = load_train_data()
    """
    imgs_train = np.load("save/data.npy")
    imgs_mask_train = np.load("save/mask.npy")
    imgs_train = imgs_train.astype('float32')
    imgs_mask_train = imgs_mask_train.astype('float32')

    total = imgs_train.shape[0]

    print('-' * 30)
    print('Creating and compiling model...')
    print('-' * 30)
    model = get_unet()
    """ START COMMENTING """
    model_checkpoint = ModelCheckpoint('unet.hdf5',
                                       monitor='loss',
                                       verbose=1,
                                       save_best_only=True)

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    model.fit(imgs_train,
              imgs_mask_train,
              batch_size=1,
              nb_epoch=3,
              verbose=1,
              shuffle=True,
              callbacks=[model_checkpoint])

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test, imgs_id_test = load_test_data()

    imgs_test = imgs_test.astype('float32')
    imgs_test -= np.load('save/mean.npy')
    imgs_test /= np.load('save/std.npy')

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model.load_weights('unet.hdf5')

    print('-' * 30)
    print('Predicting masks on test data...')
    print('-' * 30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #25
0
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train, imgs_mask_train = preprocess(imgs_train, imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    imgs_train = imgs_train[:30]
    imgs_mask_train = imgs_mask_train[:30]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_model(img_rows, img_cols)
    model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    #lrreducer = SimpleLrReducer(5, .94)
    model.fit(imgs_train, imgs_mask_train, batch_size=5, nb_epoch=250, verbose=1, shuffle=True,
              callbacks=[model_checkpoint], validation_split=0.15)

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #26
0
ファイル: train.py プロジェクト: kalpanki/nerve_segmentation
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()
    model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    model.fit(imgs_train, imgs_mask_train, batch_size=32, nb_epoch=50, verbose=1, shuffle=True,
              callbacks=[model_checkpoint])

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    tst_mean = np.mean(imgs_test)
    tst_std = np.std(imgs_test)
    imgs_test -= tst_mean
    imgs_test /= tst_std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #27
0
def run_predict():
  texts = data.load_test_data()
  input, word_index = embed.sequence_vectorize(texts)

  # Create a new model instance
  model = SavedModel()


  model.build(MODEL_PATH)

  # Re-evaluate the model
  predictions = model.predict(input)
コード例 #28
0
def run_test():
    BS = 128
    print('Loading and preprocessing test data...')
    mean, std = Learner.load_meanstd()
    
    imgs_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('Loading saved weights...')
    model = get_unet(Adam(0.001))
    print ('Loading weights from %s' % Learner.best_weight_path)
    model.load_weights(Learner.best_weight_path)
    
    print ('Augment')
    alen, dlen = len(transforms), len(imgs_test)
    test_x = np.ndarray((alen, dlen, 1, img_rows, img_cols), dtype=np.float32)
    for i in range(dlen):
        for j, transform in enumerate(transforms):
            test_x[j,i] = transform['do'](imgs_test[i].copy())
    #
    print('Predicting masks on test data...')
    outputs = []
    asis_res = model.predict(imgs_test, batch_size=BS, verbose=1)
    outputs.append(asis_res)
    for j, transform in enumerate(transforms):
        t_y = model.predict(test_x[j], batch_size=BS, verbose=1)
        outputs.append(t_y)
    #
    print('Analyzing')
    test_masks = np.ndarray((dlen, 1, img_rows, img_cols), dtype=np.float32)
    test_probs = np.ndarray((dlen, ), dtype=np.float32)
    for i in range(dlen):
        masks = np.ndarray((alen+1, 1, img_rows, img_cols), dtype=np.float32)
        probs = np.ndarray((alen+1, ), dtype=np.float32)
        for j, t_y in enumerate(outputs):
            mask, prob = t_y[0][i], t_y[1][i]
            if j:
                mask = transforms[j-1]['undo'](mask)
            masks[j] = mask
            probs[j] = prob
        #
        test_masks[i] = np.mean(masks, 0)
        test_probs[i] = np.mean(probs)
            
    print('Saving ')
    np.save(Learner.test_mask_res, test_masks)
    np.save(Learner.test_mask_exist_res, test_probs)
コード例 #29
0
ファイル: train.py プロジェクト: stayrascal/ml-fifth-division
 def test(self, model, batch_size=256):
     print('Loading and pre-processing test data...')
     imgs_test = load_test_data()
     imgs_test = preprocess(imgs_test)
     imgs_test = self.standartize(imgs_test, to_float=True)
     imgs_test = imgs_test.transpose((0, 2, 3, 1))
 
     print('Loading best saved weights...')
     model.load_weights(self.best_weight_path)
     print('Predicting masks on test data and saving...')
     imgs_mask_test = model.predict(imgs_test, batch_size=batch_size, verbose=1)
     
     np.save(self.test_mask_res, imgs_mask_test[0])
     np.save(self.test_mask_exist_res, imgs_mask_test[1])
コード例 #30
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    n_fold = 5
    k_fold_train(imgs_train, imgs_mask_train, n_fold=n_fold)

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model = get_unet()
    results = []
    for i in range(n_fold):
        model.load_weights('unet_fold%s.hdf5' % i)
        print('-' * 30)
        print('%s Predicting masks on test data...' % i)
        print('-' * 30)
        imgs_mask_test = model.predict(imgs_test, verbose=1)
        results.append(imgs_mask_test)
    imgs_mask_test = reduce(lambda x, y: x + y, results) / n_fold
    np.save('imgs_mask_test_nfold.npy', imgs_mask_test)
コード例 #31
0
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    n_fold = 5
    k_fold_train(imgs_train, imgs_mask_train, n_fold=n_fold)

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    model = get_unet()
    results = []
    for i in range(n_fold):
        model.load_weights('unet_fold%s.hdf5' % i)
        print('-' * 30)
        print('%s Predicting masks on test data...' % i)
        print('-' * 30)
        imgs_mask_test = model.predict(imgs_test, verbose=1)
        results.append(imgs_mask_test)
    imgs_mask_test = reduce(lambda x, y: x + y, results)/n_fold
    np.save('imgs_mask_test_nfold.npy', imgs_mask_test)
コード例 #32
0
def apply_mask(mode):

    print_heading('Apply mask to test images ...')

    pred_dir = os.path.join(root_path, 'preds')
    imgs_test, imgs_id_test = load_test_data(os.path.join(root_path, mode))

    for image, image_id in zip(imgs_test, imgs_id_test):
        pred_image_name = os.path.join(pred_dir, str(image_id) + '.pred.png')
        pred_image_mask = imread(pred_image_name)

        extracted = extract_image(pred_image_mask, image)
        if extracted is None:
            print("miss", image_id)
        else:
            imsave(os.path.join(pred_dir, image_id + '.result.jpg'), extracted)
コード例 #33
0
ファイル: test.py プロジェクト: juancho618/Art-CNN
def evaluate():
    model = Model()
    img_tf = tf.placeholder(tf.float32)
    with tf.Graph().as_default():
        images, labels = data.load_test_data()
        print('size', images.shape)
        x = tf.placeholder(shape=[None, data.IMAGE_SIZE, data.IMAGE_SIZE, 3],
                           dtype=tf.float32,
                           name='x')
        y = tf.placeholder(shape=[None, data.IMAGE_SIZE, data.IMAGE_SIZE],
                           dtype=tf.float32,
                           name='y')

        infered = model.inference(x,
                                  keep_prob=1.0,
                                  train=False,
                                  avg_ch_array=avg_channel_color)
        loss = model.loss(inference=infered, labels=y)
        init = tf.global_variables_initializer()
        #accuracy = model.accuracy(logits, y)
        img_tf = infered
        saver = tf.train.Saver()
        results = []
        loss_array = []
        with tf.Session() as sess:
            sess.run(init)
            saver.restore(sess, FLAGS.checkpoint_file_path)

            # total_accuracy = sess.run([accuracy])
            # print('Test Error: {}'.format(total_accuracy))
            #For every image
            for i in range(len(images)):
                offset = (i * FLAGS.batch_size) % (len(images) -
                                                   FLAGS.batch_size)
                batch_x, batch_y = images[offset:(
                    offset +
                    FLAGS.batch_size), :], labels[offset:(offset +
                                                          FLAGS.batch_size), :]
                infered_image = sess.run(infered, feed_dict={x: batch_x})
                results.append(infered_image)
                cur_loss = sess.run(loss, feed_dict={x: batch_x, y: batch_y})
                print(i, np.round(cur_loss, decimals=1))
                loss_array.append(cur_loss)
            np.save('./robe_face/results/results_robe.npy', np.array(results))
            np.save('./robe_face/results/loss_results_robe.npy',
                    np.array(loss_array))
コード例 #34
0
def train():
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    model = get_unet()
    model_checkpoint = ModelCheckpoint('weights.h5',
                                       monitor='val_loss',
                                       save_best_only=True)

    model.fit(imgs_train,
              imgs_mask_train,
              batch_size=32,
              nb_epoch=20,
              verbose=1,
              shuffle=True,
              validation_split=0.2,
              callbacks=[model_checkpoint])

    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    model.load_weights('weights.h5')

    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)

    pred_dir = 'preds'
    if not os.path.exists(pred_dir):
        os.mkdir(pred_dir)
    for image, image_id in zip(imgs_mask_test, imgs_id_test):
        image = (image[:, :, 0] * 255.).astype(np.uint8)
        imsave(os.path.join(pred_dir, str(image_id) + '_pred.png'), image)
コード例 #35
0
def just_predict(): 

    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    print('imgs_train = ',len(imgs_train))
    print('imgs_mask_train = ',len(imgs_mask_train))

    shuffle = np.random.permutation(len(imgs_train))
    imgs_train = imgs_train[shuffle]
    imgs_mask_train = imgs_mask_train[shuffle]

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization


    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    model = get_unet()



    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('weights.h5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    imgs_mask_test = postprocess(imgs_mask_test)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #36
0
def main():
    """
    The main function.
    Arguments:
        1. Takes no arguments.
    """

    train_data = data.load_training_data()
    # function call to load training data
    test_data = data.load_test_data()
    # function call to load test data
    count = CountVectorizer()
    # initialize the count vector
    tfidf_transformer = TfidfTransformer()
    # initialize a tfidf transformer
    models_dict = {}
    # empty dict
    train_tfidf = features.feature_extraction(train_data, count,
                                              tfidf_transformer)
    # function call for feature extraction
    bayes = naive_bayes(train_data, train_tfidf)
    # function call to fit the Naive Bayes classifier
    models_dict['Naive Bayes'] = bayes
    # add models to dictionary
    svm = svm_classifier(train_data, train_tfidf)
    # function call to fit SVM Classifier
    models_dict['SVM'] = svm
    # add models to a dictionary
    rand_forest = random_forest_classifier(train_data, train_tfidf)
    # function to build random forest classifier
    models_dict['Random Forest'] = rand_forest
    # add models to dictionary
    logistic = logistic_regression_classifier(train_data, train_tfidf)
    # function call to build logistic regression
    models_dict['Logistic Regression'] = logistic
    # add models to dictionary
    decision_tree = decision_tree_classifier(train_data, train_tfidf)
    # function call for decision tree classifier
    models_dict['Decision Tree'] = decision_tree
    # add model to the dictionary
    predict_test_data(train_data, test_data, models_dict, count,
                      tfidf_transformer, train_tfidf)
コード例 #37
0
ファイル: main.py プロジェクト: medric49/STFD
def test(network, submission_file):
    network.to(config.device)
    test_dataloader = data.load_test_data()

    network.eval()

    outputs = []

    with torch.no_grad():
        for image, image_id in test_dataloader:
            output = network(image)
            outputs.append((image_id[0], output[0]))

    csv_file = open(submission_file, 'w')
    csv_writer = csv.writer(csv_file)
    csv_writer.writerow(['Image_ID', 'x', 'y', 'w', 'h'])

    for image_id, output in outputs:
        csv_writer.writerow([
            image_id, output[0].item(), output[1].item(), output[2].item(),
            output[3].item()
        ])
    csv_file.close()
コード例 #38
0
ファイル: perceptron.py プロジェクト: cindy21td/Arc
def submission():
	# load the data
	v_data = load_valid_data()
	t_data = load_test_data()

	# calculate the average loss
	print "===== #1 ====="
	# ======================
	# 	Task number 1
	# ======================
	start = 0
	end = 100
	mistakes = []
	# Train from steps 0 - 1000
	for i in range(0, 10):
		# Train using the first kernel
		mistakes = train(v_data[start:end], kernel_one, 1, mistakes)
		avg = calculate_loss(end, mistakes)

		print "  Average on step ", start, "-", end , ": ", avg
		# next
		start = end
		end += 100

	print
	print "===== #2 ====="
	# ===================
	# 	Task number 2
	# ===================
	steps = 1000
	d = [1, 3, 5, 7, 10, 15, 20]
	# Try over all d
	for p in d:
		mistakes = []
		# Train using the second kernel
		mistakes = train(v_data[:steps], kernel_two, p, mistakes)
		avg = calculate_loss(steps, mistakes)

		print "  For d ", p , " steps=", steps, ": ", avg

	print
	print "===== #3 ====="
	# =====================
	# 	Task number 3
	# =====================
	# choosen d = 5
	# do kernel two first
	d = 5
	start = 0
	end = 100
	mistakes = []
	# Iterate from steps 0 - 1000
	for i in range(0, 10):
		# train using second kernel with d = 5
		mistakes = train(t_data[start:end], kernel_two, d, mistakes)
		avg = calculate_loss(end, mistakes)

		print "  d=", d, " k2 average on step ", start, "-", end , ": ", avg

		start = end
		end += 100

	print

	# do kernel three now
	sigma = 10
	start = 0
	end = 100
	mistakes = []
	# Iterate from steps 0 - 1000
	for i in range(0, 10):
		# Train using the third kernel with sigma = 10
		mistakes = train(t_data[start:end], kernel_three, sigma, mistakes)
		avg = calculate_loss(end, mistakes)

		print "  sigma=", sigma, " k3 average on step ", start, "-", end , ": ", avg

		start = end
		end += 100
コード例 #39
0
ファイル: otto-B.py プロジェクト: sronen71/otto
        verbose=1,
        max_epochs=150, 
        on_epoch_finished=[
            AdjustVariable('update_learning_rate',
                epochs=[50,100],rates=[2e-3,2e-4])],
        regularization_rate=1e-5,
        batch_iterator_train=BatchIterator(batch_size=128)
        )

    return net0



np.random.seed(1)
X,y,encoder,scaler,ids_val= data.load_train_data('data/train.csv')
X_test,ids=data.load_test_data('data/test.csv',scaler)
num_classes=len(encoder.classes_)
num_features=X.shape[1]


scores=[]
p=None

folds=5
eps=1e-4
delta=1
num=0
scores=np.zeros((y.shape[0],9))
prev_loss=10
while (delta>eps):
コード例 #40
0
ファイル: mainprog.py プロジェクト: zkytony/446-hw
def problem3():
    data = load_test_data()
    lp, le = solve3(data)
    graph_p3(lp, le)
コード例 #41
0
ファイル: train_test.py プロジェクト: mgood2/kaggle
p_keep_conv = tf.placeholder("float")
p_keep_hidden = tf.placeholder("float")
py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)

# Define loss and optimizer
cost = dice_coef_loss(py_x, Y)

train_op  = tf.train.AdamOptimizer(0.0001).minimize(cost)
predict_op = tf.argmax(py_x, 1)


print('-'*30)
print('Loading and preprocessing test data...')
print('-'*30)
imgs_test, imgs_id_test = load_test_data()
imgs_test = preprocess(imgs_test)

imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std

# Launch the graph in a session
with tf.Session() as sess:
    # you need to initialize all variables
    tf.initialize_all_variables().run()

    for i in range(100):
        training_batch = zip(range(0, len(trX), batch_size),
                             range(batch_size, len(trX), batch_size))
        for start, end in training_batch:
コード例 #42
0
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()
    
    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)
    
    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization
    
    imgs_train -= mean
    imgs_train /= std
    
    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]
    
    y_bin = np.array([mask_not_blank(mask) for mask in imgs_mask_train ])

    # X_train,X_test,y_train,y_test = train_test_split(imgs_train,imgs_mask_train,test_size=0.2,random_state=seed)
    
    skf = StratifiedKFold(y_bin, n_folds=10, shuffle=True, random_state=seed)
    for ind_tr, ind_te in skf:
        X_train = imgs_train[ind_tr]
        X_test = imgs_train[ind_te]
        y_train = imgs_mask_train[ind_tr]
        y_test = imgs_mask_train[ind_te]
        break

    X_train_flip = X_train[:,:,:,::-1]
    y_train_flip = y_train[:,:,:,::-1]
    X_train = np.concatenate((X_train,X_train_flip),axis=0)
    y_train = np.concatenate((y_train,y_train_flip),axis=0)


    X_train_flip = X_train[:,:,::-1,:]
    y_train_flip = y_train[:,:,::-1,:]
    X_train = np.concatenate((X_train,X_train_flip),axis=0)
    y_train = np.concatenate((y_train,y_train_flip),axis=0)
    
    
    imgs_train = X_train
    imgs_valid = X_test
    imgs_mask_train = y_train
    imgs_mask_valid = y_test
    
    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()
    model_name = 'unet_seed_1024_epoch_20_aug_64_80_shiftbn_sgd_srelu_plus10.hdf5'
    model_checkpoint = ModelCheckpoint('E:\\UltrasoundNerve\\'+model_name, monitor='loss', save_best_only=True)
    plot(model, to_file='E:\\UltrasoundNerve\\%s.png'%model_name.replace('.hdf5',''),show_shapes=True)
    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    augmentation=False
    batch_size=128
    nb_epoch=10
    load_model=True
    use_all_data = False
    
    if use_all_data:
        imgs_train = np.concatenate((imgs_train,imgs_valid),axis=0)
        imgs_mask_train = np.concatenate((imgs_mask_train,imgs_mask_valid),axis=0)
    
    if load_model:
        model.load_weights('E:\\UltrasoundNerve\\'+model_name)
    if not augmentation:
        # model.fit(imgs_train, imgs_mask_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, shuffle=True,
        #           callbacks=[model_checkpoint],
        #           validation_data=[imgs_valid,imgs_mask_valid]
        #           )
        pass
    else:
        
        datagen = ImageDataGenerator(
            featurewise_center=False,  # set input mean to 0 over the dataset
            samplewise_center=False,  # set each sample mean to 0
            featurewise_std_normalization=False,  # divide inputs by std of the dataset
            samplewise_std_normalization=False,  # divide each input by its std
            zca_whitening=False,  # apply ZCA whitening
            rotation_range=45,  # randomly rotate images in the range (degrees, 0 to 180)
            width_shift_range=0.0,  # randomly shift images horizontally (fraction of total width)
            height_shift_range=0.0, # randomly shift images vertically (fraction of total height)
            horizontal_flip=True,  # randomly flip images
            vertical_flip=False)  # randomly flip images
        # compute quantities required for featurewise normalization
        # (std, mean, and principal components if ZCA whitening is applied)
        datagen.fit(imgs_train)
        # fit the model on the batches generated by datagen.flow()
        model.fit_generator(datagen.flow(imgs_train, imgs_mask_train,
                            batch_size=batch_size),
                            samples_per_epoch=imgs_train.shape[0],
                            nb_epoch=nb_epoch,
                            callbacks=[model_checkpoint],
                            validation_data=(imgs_valid,imgs_mask_valid))    
    
    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)
    
    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('E:\\UltrasoundNerve\\'+model_name)

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #43
0
def train_and_predict():
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print('-' * 30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train)
    imgs_mask_train = preprocess(imgs_mask_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]
    y_hat_train = np.max(imgs_mask_train, axis=(1, 2, 3))
    print("total:", y_hat_train.shape, np.unique(y_hat_train), "label-1:", np.sum(y_hat_train))

    y_hat_train_sums = np.sum(imgs_mask_train, axis=(1, 2, 3))
    print(y_hat_train_sums)
    y_hat_train_sums_nonzero_ids = np.nonzero(y_hat_train_sums)[0]
    y_hat_train_sums = y_hat_train_sums[y_hat_train_sums_nonzero_ids]
    print(y_hat_train_sums.shape, np.min(y_hat_train_sums), np.max(y_hat_train_sums), np.mean(y_hat_train_sums))
    # y = np.bincount(y_hat_train_sums.astype('int32'))
    # ii = np.nonzero(y)[0]
    # count = y[ii]
    # from matplotlib import pyplot as plt
    # plt.plot(ii, count)
    # plt.show()
    # raw_input("pause, input any to continue")

    print('-' * 30)
    print('Fitting model...')
    print('-' * 30)
    n_fold = 5
    k_fold_train(imgs_train, y_hat_train, n_fold=n_fold)

    print('-' * 30)
    print('Loading and preprocessing test data...')
    print('-' * 30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-' * 30)
    print('Loading saved weights...')
    print('-' * 30)
    # model = get_unet()
    model = get_keras_example_net()
    results = []
    for i in range(n_fold):
        model.load_weights('unet_fold%s.hdf5' % i)
        print('-' * 30)
        print('%s Predicting masks on test data...' % i)
        print('-' * 30)
        imgs_mask_test = model.predict(imgs_test, verbose=1)
        results.append(imgs_mask_test)
    imgs_mask_test = reduce(lambda x, y: x + y, results) / n_fold
    np.save('imgs_mask_test_nfold.npy', imgs_mask_test)
コード例 #44
0
ファイル: train.py プロジェクト: sronen71/ultrasound
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)

    imgs_train, coeffs_train = load_train_data()
    masked = np.ma.masked_values(coeffs_train[:,0],0.0)

    
    imgs_train = imgs_train[~masked.mask,...]
    coeffs_train = coeffs_train[~masked.mask,...]

    imgs_train = preprocess(imgs_train)

    imgs_train = imgs_train.astype('float32')
    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean
    imgs_train /= std
    coeffs_train = coeffs_train[:,0:2]
    coeffs_train = coeffs_train.astype('float32')

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = resnet(img_rows,img_cols)
    model.compile(optimizer=Adam(lr=1e-5), loss='mean_squared_error')

    model_checkpoint = ModelCheckpoint('resnet.hdf5', monitor='loss', save_best_only=True)

    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    #print (coeffs_train)
    prog = ProgbarLogger()
    model.fit(imgs_train, coeffs_train, batch_size=64, nb_epoch=20, verbose=1, shuffle=True,
              callbacks=[prog,model_checkpoint],validation_split = 0.2)

    coeffs_train = model.predict(imgs_train, verbose=1)
    np.save('coeffs_train_predicted.npy',coeffs_train)

    print('-'*30)
    print('Loading and preprocessing valid data...')
    print('-'*30)
    imgs_valid, coeffs_valid = load_valid_data()
    imgs_valid = preprocess(imgs_valid)

    imgs_valid = imgs_valid.astype('float32')
    imgs_valid -= mean
    imgs_valid /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('resnet.hdf5')

    print('-'*30)
    print('Predicting  on valid data...')
    print('-'*30)
    coeffs_valid_predicted = model.predict(imgs_valid, verbose=1)
    np.save('coeffs_valid_predicted.npy', coeffs_valid_predicted)





    print('-'*30)
    print('Loading and preprocessing valid data...')
    print('-'*30)
    imgs_valid, coeffs_valid = load_valid_data()
    imgs_valid = preprocess(imgs_valid)

    imgs_valid = imgs_valid.astype('float32')
    imgs_valid -= mean
    imgs_valid /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting  on valid data...')
    print('-'*30)
    coeffs_valid_predicted = model.predict(imgs_valid, verbose=1)
    np.save('coeffs_valid_predicted.npy', coeffs_valid_predicted)





    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('resnet.hdf5')

    print('-'*30)
    print('Predicting  on test data...')
    print('-'*30)
    coeffs_test = model.predict(imgs_test, verbose=1)
    np.save('coeffs_test.npy', coeffs_test)

    dif = coeffs_valid[:,0:2] - coeffs_valid_predicted
    print(dif.shape)
    dif = dif[coeffs_valid[:,0]>0,:]
    print(dif.shape)	
    check = np.mean(np.sum(dif*dif,axis=1))
    print('check',check)	
コード例 #45
0
ファイル: train.py プロジェクト: supertetelman/Kaggle
def train_and_predict():
    stats = {}
    pp_head(config)
    open('config.txt', 'w').write(str(config))
    
    if  True or config['FIT']:
        pp_head('Loading and preprocessing train data...')
        imgs_train, imgs_mask_train = load_train_data()
    
        imgs_train = preprocess(imgs_train)
        imgs_mask_train = preprocess(imgs_mask_train)
    
        imgs_train = imgs_train.astype('float32')
        stats['mean'] = np.mean(imgs_train)  # mean for data centering
        stats['std'] = np.std(imgs_train)  # std for data normalization
    
        imgs_train -= stats['mean']
        imgs_train /= stats['std']
        
        imgs_mask_train = imgs_mask_train.astype('float32')
        imgs_mask_train /= 255.  # scale masks to [0, 1]
        
        open('stats.txt', 'w').write(str(stats))
    else:
        stats = eval(open('stats.txt', 'r').read()) # Read previously saved values from a file, needed to transform test images

    pp_head('Creating and compiling model...')
    if config['LOAD_MODEL']:
        model = model_from_json(open('my_model_architecture.json').read())
    else:
        model = get_unet()
        json_string = model.to_json()
        open('my_model_architecture.json', 'w').write(json_string)
    
    if config['LOAD_LAST']:
        model.load_weights('unet.hdf5')

    if config['FIT']:
        pp_head('Fitting model...')
        model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', save_best_only=True)
        model_checkpoint2 = ModelCheckpoint("weights.{epoch:02d}-{loss:.2f}.hdf5", monitor='loss', save_best_only=True)
        model.fit(imgs_train, imgs_mask_train,validation_split=config['VALIDATION'], batch_size=config['BATCH'], nb_epoch=config['EPOCH'], verbose=1, shuffle=True,
                  callbacks=[model_checkpoint,model_checkpoint2]) # batch size originally 32
    #else:
    #    model.test_on_batch(imgs_train, imgs_mask_train)

    pp_head(str(model.summary()))

    pp_head('Loading and preprocessing test data...')
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test, True)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= stats['mean']
    imgs_test /= stats['std']

    pp_head('Loading saved weights...')
    model.load_weights('unet.hdf5')

    pp_head('Predicting masks on test data...')
    imgs_mask_test = model.predict(imgs_test, verbose=1) # USe batch to speed up on large picture
    #imgs_mask_test = model.predict(imgs_test,1, verbose=1) # USe batch to speed up on large picture
    
    np.save('imgs_mask_test.npy', imgs_mask_test)
x_train, y_train_mask = load_train_data()

x_train = preprocess(x_train)
y_train_mask = preprocess(y_train_mask)

x_train = x_train.astype('float32')
mean = np.mean(x_train)
std = np.std(x_train)

x_train -= mean
x_train /= std

y_train_mask = y_train_mask.astype('float32')
y_train_mask /= 255.

unet.fit(x_train, y_train_mask, batch_size=32, epochs=20, verbose=1, shuffle=True,
          validation_split=0.2)

x_test, y_test_mask = load_test_data()
x_test = preprocess(x_test)

x_test = x_test.astype('float32')
x_test -= mean
x_test /= std

y_test_pred = unet.predict(x_test, verbose=1)

for image, image_id in zip(y_test_pred, y_test_mask):
    image = (image[:, :, 0] * 255.).astype(np.uint8)
    imsave(os.path.join(work_dir, str(image_id) + '.png'), image)
コード例 #47
0
 def setUp(self):
     self.train_data = extract_features(load_train_data())
     self.test_data = extract_features(load_test_data())
コード例 #48
0
ファイル: train.py プロジェクト: neverspill/u-net
def train_and_predict():
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    # imgs_train, imgs_mask_train = load_train_data()
    imgs_train=np.load("/mnt/data1/yihuihe/mnc/data.npy")
    imgs_mask_train=np.load("/mnt/data1/yihuihe/mnc/mask.npy")
    imgs_train = imgs_train.astype('float32')
    imgs_mask_train = imgs_mask_train.astype('float32')

    # imgs_train = preprocess(imgs_train)
    # imgs_mask_train = preprocess(imgs_mask_train)
    # print(np.histogram(imgs_train))
    # print(np.histogram(imgs_mask_train))

    total=imgs_train.shape[0]
    # imgs_train/=255.
    # mean = imgs_train.mean()# (0)[np.newaxis,:]  # mean for data centering
    # std = np.std(imgs_train)  # std for data normalization
    # imgs_train -= mean
    # imgs_train /= std

    # imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = get_unet()
    
    # model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss',verbose=1, save_best_only=True)

    # print('-'*30)
    # print('Fitting model...')
    # print('-'*30)
    # model.fit(imgs_train, imgs_mask_train, batch_size=32, nb_epoch=20, verbose=1, shuffle=True,callbacks=[model_checkpoint])
    
    # batch_size=32
    # max_iters=10000
    # for i in range(max_iters):
    #     data_batch=np.ndarray((batch_size,1,img_rows,img_cols))
    #     mask_batch=np.ndarray((batch_size,1,img_rows,img_cols))
        
    #     for img in range(batch_size):
    #         idx=np.random.randint(total)
    #         data_batch[img,0],mask_batch[img,0]=augmentation(imgs_train[idx],imgs_mask_train[idx])
    #         # plt.subplot(121)
    #         # plt.imshow(data_batch[img,0])
    #         # plt.subplot(122)
    #         # plt.imshow(mask_batch[img,0])
    #         # plt.show()
    #         data_batch-=mean
    #         data_batch/=std
    #         print(np.histogram(data_batch))
    #         print(np.histogram(mask_batch))

    #     model.train_on_batch(data_batch,mask_batch)

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test) # TODO: bug

    imgs_test = imgs_test.astype('float32')
    imgs_test -= np.load('/mnt/data1/yihuihe/mnc/mean.npy')
    imgs_test /=np.load('/mnt/data1/yihuihe/mnc/std.npy')

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('unet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)
コード例 #49
0
ファイル: train_res.py プロジェクト: neverspill/u-net
def main():
    # import time
    # start = time.time()
    # model = resnet()
    # duration = time.time() - start
    # print("{} s to make model".format(duration))

    # start = time.time()
    # model.output
    # duration = time.time() - start
    # print("{} s to get output".format(duration))

    # start = time.time()
    # model.compile(loss="categorical_crossentropy", optimizer="sgd")
    # duration = time.time() - start
    # print("{} s to get compile".format(duration))

    # current_dir = os.path.dirname(os.path.realpath(__file__))
    # model_path = os.path.join(current_dir, "resnet_50.png")
    # plot(model, to_file=model_path, show_shapes=True)
    # exit()
# -----------------------------------------------------------------------------
    print('-'*30)
    print('Loading and preprocessing train data...')
    print('-'*30)
    imgs_train, imgs_mask_train = load_train_data()

    imgs_train = preprocess(imgs_train, rows,cols)
    imgs_mask_train = preprocess(imgs_mask_train, rows/2,cols/2)

    imgs_train = imgs_train.astype('float32')
    mean = imgs_train.mean(0)[np.newaxis,:]  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization
    imgs_train -= mean
    imgs_train /= std

    imgs_mask_train = imgs_mask_train.astype('float32')
    imgs_mask_train /= 255.  # scale masks to [0, 1]

    print('-'*30)
    print('Creating and compiling model...')
    print('-'*30)
    model = resnet()
    # model.load_weights('resnet.hdf5')
    
    model_checkpoint = ModelCheckpoint('resnet.hdf5', monitor='loss',verbose=1, save_best_only=True)
# ----------------------------------------------------------------------- 
    print('-'*30)
    print('Fitting model...')
    print('-'*30)
    model.fit(imgs_train, imgs_mask_train, batch_size=32, nb_epoch=20, verbose=1, shuffle=True, callbacks=[model_checkpoint])
    # for i in range(3):
    #     model.train(imgs_train[:3],imgs_mask_train[:3])

    print('-'*30)
    print('Loading and preprocessing test data...')
    print('-'*30)
    imgs_test, imgs_id_test = load_test_data()
    imgs_test = preprocess(imgs_test, rows,cols)

    imgs_test = imgs_test.astype('float32')
    imgs_test -= mean
    imgs_test /= std

    print('-'*30)
    print('Loading saved weights...')
    print('-'*30)
    model.load_weights('resnet.hdf5')

    print('-'*30)
    print('Predicting masks on test data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_test, verbose=1)
    np.save('imgs_mask_test.npy', imgs_mask_test)

    print('-'*30)
    print('Predicting masks on train data...')
    print('-'*30)
    imgs_mask_test = model.predict(imgs_train, verbose=1)
    np.save('imgs_train_pred.npy', imgs_mask_test)