First simple word counter. Doesn't work on pride-prejudice -- OOM. It works on half of it.

35-dnn-no-learning/count_words_binary_encoding_no_learning.py

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+# -*- coding: utf-8 -*-
+'''
+# An implementation of deep learning for counting symbols
+Input:  [10, 12, 10, 11, 2, 2, 2, 1, 1]
+Output: words=[2, 10, 1, 12, 11] counts=[3, 2, 2, 1, 1] (Not necessarily in this order)
+
+'''  # noqa
+
+from __future__ import print_function
+from keras.models import Sequential, Model
+from keras import layers, metrics
+from keras import backend as K
+from keras.utils import plot_model
+from keras.utils import to_categorical
+import numpy as np
+import math
+from six.moves import range
+import string, re, collections, os, sys, operator
+
+stopwords = set(open('../stop_words.txt').read().split(','))
+all_words = re.findall('[a-z]{2,}', open(sys.argv[1]).read().lower())
+words = [w for w in all_words if w not in stopwords]
+
+uniqs = [''] + list(set(words))
+uniqs_indices = dict((w, i) for i, w in enumerate(uniqs))
+indices_uniqs = dict((i, w) for i, w in enumerate(uniqs))
+
+indices = [uniqs_indices[w] for w in words]
+
+WORDS_SIZE = len(words)
+VOCAB_SIZE = len(uniqs)
+BIN_SIZE = math.ceil(math.log(VOCAB_SIZE, 2))
+
+def encode_binary(W):
+    x = np.zeros((1, WORDS_SIZE, BIN_SIZE, 1))
+    for i, w in enumerate(W):
+        for n in range(BIN_SIZE): 
+            n2 = pow(2, n)
+            x[0, i, n, 0] = 1 if (w & n2) == n2 else 0
+    return x
+
+print(f'Words size {WORDS_SIZE}, vocab size {VOCAB_SIZE}, bin size {BIN_SIZE}')
+#print(f'Words={words}')
+#print(f'Uniqs={uniqs}')
+#print(f'Indices={indices}')
+
+def set_weights(clayer):
+    wb = []
+    b = np.zeros((VOCAB_SIZE), dtype=np.float32)
+    w = np.zeros((1, BIN_SIZE, 1, VOCAB_SIZE), dtype=np.float32)
+    for i in range(VOCAB_SIZE):
+        for n in range(BIN_SIZE):
+            n2 = pow(2, n)
+            w[0][n][0][i] = 1 if (i & n2) == n2 else -1 #-(BIN_SIZE-1)
+    for i in range(VOCAB_SIZE):
+        slice_1 = w[0, :, 0, i]
+        n_ones = len(slice_1[ slice_1 == 1 ])
+        if n_ones > 0: slice_1[ slice_1 == 1 ] = 1./n_ones 
+        n_ones = len(slice_1[ slice_1 == -1 ])
+        if n_ones > 0: slice_1[ slice_1 == -1 ] = -1./n_ones 
+    # Scale the whole thing down one order of magnitude
+    #w = w * 0.1
+    wb.append(w)
+    wb.append(b)
+    clayer.set_weights(wb)
+
+def Max(x):
+    zeros = K.zeros_like(x)
+    return K.switch(K.less(x, 0.9), zeros, x)
+
+def sigmoid_steep(x):
+    base = K.ones_like(x) * pow(10, 20)
+    return 1. / (1. + K.pow(base, -x))
+
+def Max2(x):
+    return sigmoid_steep(x - (1-1/BIN_SIZE))  * x
+
+def Reduce(x):
+    return K.pow(x, 15)
+
+def SumPooling2D(x):
+    return K.sum(x, axis = 1) 
+
+def model_convnet2D():
+    print('Build model...')
+    model = Sequential()
+    model.add(layers.Conv2D(VOCAB_SIZE, (1, BIN_SIZE),  input_shape=(WORDS_SIZE, BIN_SIZE, 1)))
+    set_weights(model.layers[0])
+    model.add(layers.ReLU(threshold=1-1/BIN_SIZE))
+#    model.add(layers.Lambda(Max))
+#    model.add(layers.Lambda(Max2))
+#    model.add(layers.Lambda(Reduce))
+    model.add(layers.Lambda(SumPooling2D))
+    model.add(layers.Reshape((VOCAB_SIZE,)))
+
+    return model, "words-nolearning-{}v-{}f".format(VOCAB_SIZE, BIN_SIZE)
+
+
+model, name = model_convnet2D()
+model.summary()
+plot_model(model, to_file=name + '.png', show_shapes=True)
+
+batch_x = encode_binary(indices)
+
+intermediate_model = Model(inputs=model.input, outputs=[l.output for l in model.layers])
+preds = intermediate_model.predict(batch_x) # outputs a list of 4 arrays
+
+prediction = preds[-1][0] # -1 is the output of the last layer
+ 
+for w, c in sorted(list(zip(uniqs, prediction)), key = operator.itemgetter(1), reverse=True)[:25]:
+    print(w + " - " + str(c))
+