Guenevera Tunberg
Multi tool use
Guenevera Tunberg
Res apud Vicidata repertae:
Guenevera Morrish Tunberg (Anglice Jennifer Morrish Tunberg) est profestrix apud Universitatem Kentuckianam et uxor Terentii Tunberg, cum quo scripsit nonnullos libros Neolatinos.
Verbis suis:
Guenevera Morrish Tunberg, doctricis rerum gestarum diplomate in studiorum universitate Oxoniensi honestata, medii aevi historiam, mores, litteras docuit tam in Canada quam in Belgica. Profestrix nunc adiutans apud litterarum classicarum facultatem collegiumque “honors” nominatum docendi muneribus Lexintoniae in universitate Kentukiana fungitur, ubi operam solet litteris antiquis, mediaevalibus, Neolatinis dare.
– Guenevera Morrish Tunberg, Cattus Petasatus, p. 67.
Eius scripta |
- 2000: "Dr Seuss, Cattus Petasatus", in Latinum verterunt Guenevera Tunberg et Terentius Tunberg, ed. Bolchazy-Carducci, 2000, 75 p.
Nexus interni
- Arbor Alma
- Cattus Petasatus
- Libri Latine redditi
- Quomodo Invidiosulus Nomine Grinchus Christi Natalem Abrogaverit
- Virent Ova! Viret Perna!
Nexus externi |
- Apud Universitatem Kentuckianam
- Apud Amazon.com
De hac re nexus intervici usque adhuc absunt. Adde, si reppereris.
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
Guenevera Tunberg
De hac re nexus intervici usque adhuc absunt. Adde, si reppereris.
