Vladimirus Nemirovič-Dančenko

Multi tool use

Vladimirus Nemirovič-Dančenko
Res apud Vicidata repertae:
Nativitas:
11 Decembris 1858;
OzurgetiObitus:
25 Aprilis 1943;
MoscuaPatria:
Imperium Russicum, Unio Sovietica
Officium
Munus: playwright, dramaturge, director, theatre critic, scriptor scaenicus, theatre director, opera director, drama teacher
Patronus: Moscow Art Theatre
Memoria
Laurae: Stalin prize, Order of Lenin, People's Artist of the USSR, Order of the Red Banner of Labour, People's Artist of the RSFSR, Order of the Red Eagle
Sepultura: Novodevichy Cemetery
Vladimirus Ioannis filius Nemirovič-Dančenko (Russice Владимир Иванович Немирович-Данченко, tr. Vladimir Ivanovič Nemirovič-Dančenko; natus 23 Decembris 1858 in urbe Ozurgeti tunc gubernii Kutaisensis Imperii Russici, nunc Georgiae, mortuus 25 Aprilis 1943 Moscuae) fuit clarus moderator, paedagogus ac scriptor scaenicus Russicus et Sovieticus.
In gymnasio Triphelis et dein in Universitate Moscuensi educatus, initio fuerat criticus theatralis, dein factus est scriptor scaenicus. Anno 1898, cum Constantino Stanislavskij, Theatrum Artificiosum Moscuense (Russice Московский художественный театр, tr. Moskovskij chudožestvennyj teatr) condidit, cuius praefectus ad finem vitae suae erat. Anno 1936 titulo Artificis Popularis URSS, annis 1942 et 1943 Praemio Staliniano dignatus est.
Nexus externi |

|
Vicimedia Communia plura habent quae ad Vladimirum Nemirovič-Dančenko spectant.
|
Pagina de Vladimiro Nemirovič-Dančenko Encyclopaediae Sovieticae Magnae editionis tertiae .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}
(Russice)
.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 biographiam spectat. Amplifica, si potes!
|
5qa6 YMX2h4WWA0vfNLmxRsF,o3YDHuVI30nX
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...