Arzen (-is, m.) (seu Villa de Arzene) (Italiane: Arzene) est vicus Italiae et fractio municipii Valvasonis Arzenis, in Regione Foro Iulii-Venetia Iulia ac in Provincia Portusnaonensi situm. Fuit municipium usque ad diem 1 Ianuarii 2015. Incolae Arzenenses appellantur.
Index
1Historia
2Fractiones in municipio abrogato Arzenis
3Nexus interni
4Nexus externi
5Pinacotheca
6Notae
Historia |
Die 1 Ianuarii 2015 Arzen coniunxit se cum Valvasonis, ut constituiret municipium novum Valvasonis Arzenis[1].
Fractiones in municipio abrogato Arzenis |
San Lorenzo.
Nexus interni
Forum Iulii-Venetia Iulia (regio),
Forum Iulii (terra),
Provincia Portusnaonensis,
Portus Naonis (urbs),
Valvason Arzen (municipium).
Nexus externi |
Situs Publicus .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} (Italiane)
Vicimedia Communia plura habent quae ad Arzenem spectant.
Pinacotheca |
Collocatio finium Municipii in Provincia Portusnaonensi.
Notae |
↑Communia nova Italiae condita anno 2015: tuttitalia.it.
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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...
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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...
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...