Tabula (Anglice signata) itineris navis Descubierta, Malaspina duce, reditu a Tonga ad Hispaniam ommisso; iter navis Atrevida, Bustamante duce, paene idem fuit.
Expeditio Malaspinae (1789–1794) fuit exploratio scientifica quae per circumnavigationem annorum quinque, Alexandro Malaspina et Iosepho de Bustamante y Guerra ducibus, facta est. Quamquam expeditio ex Malaspina appellatur, hic semper duci Bustamante partem aequalem tribuit; ille autem Malaspinam fuisse caput expeditionis ex initio fasus est.[1]
Gubernatio Hispanica pecuniam expeditioni concessit, cui proposita definite scientifica instituit, eodem modo navigationum Iacobi Cook et Ioannis Francisci de Galaup, comitis de La Pérouse. Scientiae periti Expeditionis Malaspinae conlectiones fecerunt quae tantum conlectiones expeditionis Iacobi Cook fortasse exsuperabant, sed ob societatem Malaspinae nuper rediti in coniuratione ad deiciendam gubernationem, ipsoque in carcerem coniecto, plurimae expeditionis relationes et conlectiones, ineditae conservatae, primum saeculo undevicensimo exeunte vulgatae sunt.[2]
Index
1Nexus interni
2Notae
3Bibliographia
4Nexus externi
Nexus interni
Alexander Malaspina
Exploratio Hispanica Boreoccidentis Pacifici
Notae |
↑Cutter, Donald C. (1991). Malaspina & Galiano: Spanish Voyages to the Northwest Coast, 1791 & 1792. University of Washington Press. pp. 4–5. ISBN 0-295-97105-3
↑Fernandez-Armesto, Felipe (2006). Pathfinders: A Global History of Exploration. W.W. Norton & Company. pp. 305–307. ISBN 0-393-06259-7
Bibliographia |
Meany, Edmond Stephen. Vancouver's discovery of Puget Sound. Mystic Seaport.
Nexus externi |
Biographia Malaspinae, a Dario Manfredi scripta, apud web.viu.ca
Circumnavigatio Malaspinae: "Global change and exploration of the ocean's biodiversity," apud www.expedicionmalaqspina.es
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...
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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...
