La conquête de Constantinople

Multi tool use

Pagina prima libri manu scripti
La conquête de Constantinople: Bibliotheca Nationalis Francica MS Français 24210 f. 1r

Editio bilinguis huius operis a Blasio de Vigenère confecta, Lutetiae anno 1585 divulgata
La conquête de Constantinople est opus Francogallice anno fere 1208 a Galfrido de Villa Harduini scriptum de rebus gestis a Cruce signatis quartae expeditionis sacrae et ab imperatoribus Balduino I et Henrico usque ad annum 1208. Huic operi continuationem praebet liber titulo Histoire de l'empereur Henri de Constantinople ab Henrico de Valencenis scriptus.
Notae |
Bibliographia |
- Editiones et versiones
- Paulin Paris, ed., Geoffroy de Villehardouin et Henri de Valenciennes: De la Conqueste de Constantinople. Lutetiae, 1838 .mw-parser-output .existinglinksgray a,.mw-parser-output .existinglinksgray a:visited{color:gray}.mw-parser-output .existinglinksgray a.new{color:#ba0000}.mw-parser-output .existinglinksgray a.new:visited{color:#a55858}
(Lingua Francogallica antiqua) Textus apud gallica
- Natalis de Wailly, ed., Geoffroy de Ville-Hardouin: Conquête de Constantinople, avec la continuation de Henri de Valenciennes. 3a ed. Lutetiae: Didot, 1882
(Francogallice, lingua Francogallica antiqua) Textus apud archive.org
- Edmond Faral, ed., Villehardouin: La conquête de Constantinople. Lutetiae: Les Belles Lettres, 1938. 2 voll.
(Francogallice, lingua Francogallica antiqua)
- Historica et critica
- Jeanette M. A. Beer, Villehardouin: Epic Historian. Genavae: Droz, 1968
Nexus externi |
- "Geoffroi de Villehardouin" apud arlima.net
- Versio Anglica
- Textus imperfectus Francogallicus tironibus explicatus
U19JiiYa7U21ZKvWBv4,Ct 5dAaQJQVXmCi0,pjMso,R,8UMmP654DFb2DMxxTKj1muaRUIdX2 in7fMEv,NIuH V,BBb7E8p,btbjh
Popular posts from this blog
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
1
$begingroup$
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...
1
$begingroup$
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...