Administer Foederalis Aerarii Publici
Multi tool use
Administer Foederalis Aerarii Publici in Germania vectigalia conligenda et pecunias reipublicae distribuendas decernit. Aministerium Aerarii Publici (Theodisce: "Bundesministerium der Finanzen") anno 1949 conditum est.
Ministri aerarii publici |
Adhuc hi fuerunt administri aerarii:
Fridericus Schäffer (CSU) 1949–1957
Franciscus Etzel (CDU) 1957–1961
Henricus Starke (CDU) 1961–1962
Rudolphus Dahlgrün (FDP) 1962–1966
Curtius Schmücker (CDU) 1966
Franciscus Iosephus Strauß (CSU) 1966–1969
Helimutus Schmidt (SPD) 1972–1974
Ioannes Apel (SPD) 1974–1978
Ioannes Matthöfer (SPD) 1978–1982
Manfredus Lahnstein (SPD) 1982
Gerhardus Stoltenberg (CDU) 1982–1989
Theodorus Waigel (CSU) 1989–1998
Anscharius Lafontaine (SPD) 1998–1999
Ioannes Eichel (SPD) 1999–2005
Petrus Steinbrück (SPD) 2005–2009
Volfgangus Schäuble (CDU) 2009–2017
Petrus Altmaier (CDU, pro Tempore) 2017 - 2018
Olaus Scholz (SPD) 2018 -
Nexus externus |
Pagina officialis
Nexus interni
- Index institutionum germanarum
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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...
