update 27

lib/scrap.py

@@ -1,82 +0,0 @@
-    from nolearn.lasagne import NeuralNet, BatchIterator
-    from lasagne.layers import InputLayer as Input
-    from lasagne.layers import DropoutLayer as Dropout
-    from lasagne.layers import DenseLayer as Dense
-    from lasagne.layers import RecurrentLayer as RNN
-    from lasagne.layers import LSTMLayer as LSTM
-    from lasagne.layers import GRULayer as GRU
-    from lasagne.layers import ReshapeLayer as Reshape
-    from lasagne.layers import EmbeddingLayer as Embedding
-    from lasagne.layers import NonlinearityLayer as Nonlinearity
-    from lasagne.nonlinearities import softmax
-    from lasagne.objectives import categorical_crossentropy
-    from lasagne.updates import adam
-
-
-        elif 0:
-            # TODO: consider using to_categorical instead
-            #from keras.utils.np_utils import to_categorical
-            x = np.array(seq_text[:newlen], dtype='int32').reshape((-1, seq_length))
-            # Generate input and output per substring, as an indicator matrix.
-            y = np.zeros((x.shape[0], x.shape[1], self.vocab_size), dtype='bool')
-            for i in np.arange(x.shape[0]):
-                for j in np.arange(x.shape[1]):
-                    y[i, j, x[i, j]] = 1
-
-            # push a padding character to the front of the inputs.
-            # this effectively maps x[a,b,c]'s next character to y[a,b,c]
-            x = np.roll(y, 1, axis=1)
-            x[:, 0, :] = 0
-            x[:, 0, 0] = 1
-
-            vx = x
-            vy = y
-        else:
-            # TODO: handle dtype more elegantly
-            #vx = seq_text[0:newlen + 0]
-            #vy = seq_text[1:newlen + 1]
-            vy = seq_text[:newlen]
-            vx = np.roll(vy, 1)
-            vx[0] = 0 # remember we assert that 0 corresponds to PADDING in Translator
-
-            # stuff in the original LanguageModel.lua that we don't need
-            #vx = vx.reshape(batch_size, -1, self.seq_length)
-            #vy = vy.reshape(batch_size, -1, self.seq_length)
-            #vx = vx.transpose([1, 0, 2])
-            #vy = vy.transpose([1, 0, 2])
-
-            # this is good enough
-            vx = vx.reshape(-1, self.seq_length)
-            vy = vy.reshape(-1, self.seq_length)
-            vx = np.array(vx, dtype=np.uint8)
-            vy = np.array(vy, dtype=np.uint8)
-
-
-
-    sentence = text[start_index:start_index + seq_length]
-    generated = sentence
-    lament('## Generating')
-    sys.stdout.write(generated)
-    sys.stdout.write('~') # PADDING
-
-    was_pad = True
-    for i in range(sample_chars):
-        x = np.zeros((1, seq_length, textmap.vocab_size), dtype=np.bool)
-        for t, char in enumerate(sentence):
-            x[0, t, textmap.map(char)] = 1
-
-        preds = model.predict(x, batch_size=1, verbose=0)[0]
-        next_index = asample(preds, temperature)
-        next_char = textmap.unmap(next_index)[0]
-
-        generated += next_char
-        sentence = sentence[1:] + next_char
-
-        is_pad = next_char == PADDING
-        sys.stdout.write(next_char)
-        if is_pad and not was_pad:
-            sys.stdout.write('\n')
-        sys.stdout.flush()
-
-        was_pad = is_pad
-    lament()