-2 Latinitas huius rei dubia est. Corrige si potes. Vide {{latinitas}}.
Agitator status solidi est exemplum memoriae non volatilis.
Tergum partis computatri ENIAC tubos vacui monstrant.
Memoria computatralis est artificium vel apparatus computatralis qui datorum retentionem eorumque praebitionem in computatro efficit. Quae est apparatus electronicus internus, idoneus ad data conservanda, quae sive parumper sive constanter disciplina operationali requiruntur. Vt exempla ponantur, documenta digitalia, photopicturas, et multos bitinummos in memoria computatrali habere possumus. Priscum memoriae computatralis synonymum est copia.[1]
Memoriam volatilem aut non volatilem (stativam) habet. Memoria non volatilis est fixa (tamquam discus fixus, qui in omnibus computatris reperitur) aut mobilis (tamquam memoria USB et discus flexibilis).
Sunt duo principalia memoriae semiconductrorum memoriae: memoria volatilis et memoria non volatilis. Inter exempla memoriae non volatilis sunt memoria fulgurea (pro memoria secundaria adhibita) ac ROM, PROM, EPROM, et EEPROM.
Vocabulum memoria tantum in computatione memoriam volatilem plerumque attingit.
Index
1Nexus interni
2Notae
3Bibliographia
4Nexus externi
Nexus interni
Charta memorialis
Charta perforata
Circuitus integratus
Data digitalia
Discus fixus
Discus opticus
Electronica
Endianness
Geometria memoriae
Hibernatio (computatio)
Hierarchia memoriae
Initiatio systematis
Memoria deterministica
Memoria holographica
Memoria semiconductrorum
Memoria USB
Memoria volatilis dynamica
Memoria volatilis statica
Memoria volatilis
Memoria virtualis
Redintegratio memoriae
Remanentia datorum
Taeniola chartacea
Taeniola magnetica
Transistrum
Notae |
↑Anglice store. A. M. Turing et R. A. Brooker (1952), Programmer's Handbook for Manchester Electronic Computer Mark II, University of Manchester.
Bibliographia |
Miller, Stephen W. 1977. Memory and Storage Technology. Montvale: AFIPS Press.
Stanek, William R. 2009. Windows Server 2008 Inside Out. O'Reilly Media, Inc. ISBN 9780735638068.
Time Life Books. 1988 Memory and Storage Technology. Alexandriae Virginiae: Time Life Books.
Nexus externi |
Vicimedia Communia plura habent quae ad memoriam computatralem spectant.
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...