dense, shallow, monolithic

37-dense-shallow-monolithic/tf-37.py

@@ -0,0 +1,90 @@
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy as np
+import sys, os, string
+
+characters = string.printable
+char_indices = dict((c, i) for i, c in enumerate(characters))
+indices_char = dict((i, c) for i, c in enumerate(characters))
+
+INPUT_VOCAB_SIZE = len(characters)
+LINE_SIZE = 80
+
+def encode_one_hot(line):
+    x = np.zeros((1, LINE_SIZE, INPUT_VOCAB_SIZE))
+    sp_idx = char_indices[' ']
+    for i, c in enumerate(line):
+        index = char_indices[c] if c in characters else sp_idx
+        x[0][i][index] = 1 
+    # Pad with spaces
+    for i in range(len(line), LINE_SIZE):
+        x[0][i][sp_idx] = 1 
+    return x.reshape([1, LINE_SIZE*INPUT_VOCAB_SIZE])
+
+def decode_one_hot(y):
+    s = []
+    x = y.reshape([1, LINE_SIZE, INPUT_VOCAB_SIZE])
+    for onehot in x[0]:
+        one_index = np.argmax(onehot) 
+        s.append(indices_char[one_index]) 
+    return ''.join(s)
+    
+def normalization_layer_set_weights(n_layer):
+    print(n_layer.get_weights()[0].shape)
+    print(n_layer.get_weights()[1].shape)
+    wb = []
+    w = np.zeros((LINE_SIZE*INPUT_VOCAB_SIZE, LINE_SIZE*INPUT_VOCAB_SIZE), dtype=np.float32)
+    b = np.zeros((LINE_SIZE*INPUT_VOCAB_SIZE), dtype=np.float32)
+    # Let lower case letters go through
+    for r in range(0, LINE_SIZE*INPUT_VOCAB_SIZE, INPUT_VOCAB_SIZE):
+        for c in string.ascii_lowercase:
+            i = char_indices[c]
+            w[r+i, r+i] = 1
+        # Map capitals to lower case
+        for c in string.ascii_uppercase:
+            i = char_indices[c]
+            il = char_indices[c.lower()]
+            w[r+i, r+il] = 1
+        # Map all non-letters to space
+        sp_idx = char_indices[' ']
+        for c in [c for c in list(string.printable) if c not in list(string.ascii_letters)]:
+            i = char_indices[c]
+            w[r+i, r+sp_idx] = 1
+        # Map single letters to space
+        previous_c = r-INPUT_VOCAB_SIZE
+        next_c = r+INPUT_VOCAB_SIZE
+        for c in [c for c in list(string.printable) if c not in list(string.ascii_letters)]:
+            i = char_indices[c]
+            if r > 0 and r < (LINE_SIZE-1)*INPUT_VOCAB_SIZE: 
+                w[previous_c+i, r+sp_idx] = 0.75
+                w[next_c+i, r+sp_idx] = 0.75
+            if r == 0:
+                w[next_c+i, r+sp_idx] = 1.5
+            if r == (LINE_SIZE-1)*INPUT_VOCAB_SIZE:
+                w[previous_c+i, r+sp_idx] = 1.5
+
+    wb.append(w)
+    wb.append(b)
+    n_layer.set_weights(wb)
+    return n_layer
+
+def build_model():
+    # Normalize characters using a dense layer
+    model = Sequential()
+    dense_layer = Dense(LINE_SIZE*INPUT_VOCAB_SIZE, 
+                        input_shape=(LINE_SIZE*INPUT_VOCAB_SIZE,),
+                        activation='softmax')
+    model.add(dense_layer)
+    return model
+
+model = build_model()
+model.summary()
+normalization_layer_set_weights(model.layers[0])
+
+with open(sys.argv[1]) as f:
+    for line in f:
+        if line.isspace(): continue
+        batch = encode_one_hot(line)
+        preds = model.predict(batch)
+        normal = decode_one_hot(preds)
+        print(normal)