Praefectus: Douglas Robert Ford Consilium: Parliament of Ontario
Vita
Incolae: 14 279 196 Sermo publicus: Lingua Anglica Zona horaria: UTC-5, UTC-6, America/Toronto
Tabula aut despectus
Vide etiam paginam discretivam: Ontario (discretiva)
Ontario (-onis, f.)[1][2] est provincia Canadae. Provinciae urbs maxima et caput est Torontum.
Index
1Urbes
2Flumina
3Lacus
4Sententia
5Notae
6Bibliographia
7Nexus externus
Urbes |
Hamiltonium
Londinium
Ottava
Sinus Tonitralis
Torontum, olim Eboracum (York)
S. Mariae Cadit (Canada)
Flumina |
Albania
Sabrina
Sancti Laurentii Flumen
Lacus |
Lacus Magni
Sententia |
"Ut incepit fidelis sic permanet" (Anglice: Loyal she began, loyal she remains).
Notae |
↑Vide nomina biologica asteraceae Symphyotrichi ontarionis (Asterae ontarionis), annelidi Rhyacodrilis ontarionis, coleopteri Agabi fuscipennis ontarionis, et parasiti Baryodma ontarionis.
↑Latine et "Ontarium": vide hoc Civitatis Vaticanae documentum: http://www.vatican.va/archive/aas/documents/AAS%2007%20[1915]%20-%20ocr.pdf (... in Ontarii provincia Canadensi ...)
Bibliographia |
Aestas in Sandbanks Reservatione Provinciali prope Andiatrocum Lacum (Ontarionem Lacum)
Niagara Vallum (rupes) in Bruce Paeninsula.
Baskerville, Peter A. 2005. Sites of Power: A Concise History of Ontario. Oxford University Press. (Editio prima est Ontario: Image, Identity and Power, 2002). Commentarius de libro.
Beckett, Harry (2001). Ontario. Weigl. ISBN 189699085
Celebrating One Thousand Years of Ontario's History: Proceedings of the Celebrating One Thousand Years of Ontario's History Symposium, April 14, 15, and 16, 2000. 2000. Ontario Historical Society.
Chambers, Lori, et Edgar-Andre Montigny, eds. 2000. Ontario Since Confederation: A Reader.
Montigny, Edgar-André; Chambers,, Anne Lorene (2000). Ontario since Confederation : a reader. University of Toronto Press. ISBN 0802044441
White, Randall (1985). Ontario, 1610-1985 : a political and economic history. Dundurn Press. ISBN 0919670989
Nexus externus |
Vicimedia Communia plura habent quae ad Ontarionem spectant.
Provinciae et territoria Canadae
Provinciae
Alberta • Columbia Britannica • Insula Principis Eduardi • Manitoba • Nova Scotia • Novum Brunsvicum • Ontario • Quebecum • Saskatchewan • Terra Nova et Estotilandia
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...
Ontario
