Anfa
Multi tool use
Coordinata: .mw-parser-output .geo-default,.mw-parser-output .geo-dms,.mw-parser-output .geo-dec{display:inline}.mw-parser-output .geo-nondefault,.mw-parser-output .geo-multi-punct{display:none}.mw-parser-output .longitude,.mw-parser-output .latitude{white-space:nowrap}
33°35′57″N 7°37′12″W / 33.59917°N 7.62°W / 33.59917; -7.62
Anfa
Res apud Vicidata repertae:
Civitas:
MarocumNumerus incolarum:
4 370 000Zona horaria:
UTCSitus interretialis
Geographia
Superficies: 386±1 chiliometrum quadratum
Coniunctiones urbium
Urbes gemellae: Alexandria, Athenae, Burdigala, Cairus, Sicagum, Dubai7, Olisipo, Constantinopolis, Jakarta, Kuala Lumpur, Urbs Fluminensis, Algeria, Mons Regius, Petropolis, Sciamhaevum, Sosnowiec, Tokium, Bengalurus, Thaenae, Iedda, Tingis, Artvin, Busan, Oran
Anfa[1] seu linguis Europaeis Casablanca est urbs maxima Maroci.
Notae |
↑ "Anfa, quibusdam Anaffa": Georg Braun, Frans Hogenberg, Civitates Orbis Terrarum vol. 1 (1572). Cf. imaginem nostram
Nexus externi |
"Anfa, quibusdam Anaffa" anno 1572 delineata (Georg Braun, Frans Hogenberg,
Civitates Orbis Terrarum vol. 1)
|
Vicimedia Communia plura habent quae ad Casablancam spectant.
|
.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 urbem spectat. Amplifica, si potes!
|
Africae urbes milies milium incolarum
Aba • Abidjan • Accra • Alexandria • Algeria • Anfa • Antananarivo • Bamako • Beninum • Brazzapolis • Cairus • Canos • al-Chartum Bahri • Civitas Capitis • Conakry • Dakar • Dar es Salaam • Duala • Durbanum • Ekurhuleni • Fezza • Giza • Harare • Ibadan • Ilorin • Ioannesburgum • Kaduna • Kampala • Kananga • Khartum • Kigali • Kinshasa • Kumasi • Lacupolis • Luanda • Lubumbashi • Lusaka • Maiduguri • Maputo • Mbuji-Mayi • Mogadiscio • Monrovia • Nairobia • Ndjamena • Neanthopolis • Niamey • Omdurman • Portus Harcurtensis • Praetoria • Rabatum • Schubra al-Chaima • Soweto • Tripolis • Tunes • Uagadugu • Yaunde
Opus geopoliticum • Maximae urbes orbis terrarum
Capsae cognatae: Urbes Africae capitales • Urbes Americae Australis maximae • Urbes Americae Septentrionalis maximae • Urbes Asiae maximae • Urbes Europae maximae • Urbes Oceaniae maximae
lg9J7tqZ9jHZCT6nY4 yyIzf5UmjY71prn,X
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...
Anfa
