Universitas Iagellonica

Multi tool use

Universitas Iagellonica
Res apud Vicidata repertae:
universitas publicaCivitas:
PoloniaSitus:
Cracovia
Rectio
Situs interretialis
Praetorium: Cracovia;
Origo
Conditor: Casimirus III
Inceptio: 12 Maii 1364
Commemoratio
Adagium: Plus ratio quam vis
Universitas Iagellonica Cracoviensis [1] est antiquissima universitatum Poloniae et una antiquarum in toto mundo (in Media Europa, tantum Universitas Carolina Pragensis est antiquior). Condita est die 12 Maii anno 1364 a Casimiro Magno, rege Poloniae, ut Academia Cracoviensis ad instar Universitatis Bononiensis cum tribus facultatibus (sine facultate theologica). Anno vero 1400 sumptu et cura sanctae Hedvigis reginae Poloniae restituta cum facultate theologica ut universitas.
Universitatis nomen a Ladislao Iagiello sanctae Hedvigis mariti procedit. Ideo una celeberrimum universitatum nominatur a rege qui artem scribendi legendique non habuerit, sed unus optimorum regum Poloniae erat.
Universitas Cracoviensis multos et claros professores et studiosos habebat, ad quos numerantur:
Nicolaus Copernicus, astronomus theoriae heliocentricae creator
- sanctus Ioannes Cantius
Ioannes Cochanovius, poeata clarissimus Polonus
- Ioannes Paulus II
- Vislava Szymborska
et alii.
Celebris est Bibliotheca Iagellonica quoque.
Notae |
↑ google books: Universitas Iagellonica Cracoviensis acta scientarum litterarumque: Schedae litterariae
Nexus externus |

|
Vicimedia Communia plura habent quae ad Universitas Iagellonica spectant.
|
Statut Uniwersytetu Jagiellońskiego .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}
(Polonice)
.mw-parser-output .stipula{padding:3px;background:#F7F8FF;border:1px solid grey;margin:auto}.mw-parser-output .stipula td.cell1{background:transparent;color:white}

|
Haec stipula ad universitatem aut scholam spectat. Amplifica, si potes!
|
7 Ooz1ykNTE9Qb,g0TM1c DT7W7CEUe eJVn o,K8BjPX0H9YQD ofLsg NCylkPP,c,yyZ5 JlhrTzPv0PiXDHt7h uC9Ds
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...