update 26

lib/scrap.py

@@ -0,0 +1,82 @@
+    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()