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Chemia Organometallica , disciplina quae partes chemiae organicae inorganicaeque complectitur; moleculas noscit quae vincula covalentes inter carbonium et metallum aut semimetallum continent. Index 1 Exempla 2 Proprietas chemicarum organometallicarum 3 Ramae chemiae organometallicae 4 Fons Exempla | Vitaminum B12 in corpore humano est chemica organometallica, sed non omnes moleculae metallum habentes sunt organometallicae, solum eae in quo vinculum inter carbonium et metallum est covalentum. Ergo, quamquam natriumacetatum (H 3 C-COONa, sal natrii acoris aceti) partem metallicum et partem organicam CH 3 habet, molecula ipsa non est metallorganica, quia vinculum natrii est ionicum et non est inter natrium et carbonium. Similiter chlorophyll et haemoglobinum non sunt chemicae metallorganicae. Etiam hodie, chemica Grignardi qui magnesium continent sunt potior pro synthesis moleculae organicae. Pro harum chemicarum inventa Victori Grignard Donatium No...

8 0 I'd like to make a disclaimer that I remember very little about this book or when I read it (I think I read it sometime in the late 90's or early 2000's), so it'll probably be difficult to figure out what it is, but it's bothered me on occasion for a few years. I want to say that it's from the 90's, but it could be later or much earlier than that. I really only remember bits and pieces of it, primarily the beginning. It starts out in the woods, where someone is running away from/stumbles upon a cult ritual. The cult members are wearing red cloaks, and I think that they catch the person and kill them. The main part of the book starts with a kid moving in to his uncle's(?) house. Aside from this I really can't remember much, but I think at one point the kid navigates a maze...

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...