Change the name of this style to dense, because it's focusing on the dense layer

35-dense/tf-35-multiple.py

@@ -0,0 +1,92 @@
+from keras.models import Model
+from keras import layers
+from keras.layers import Input, Dense
+from keras.utils import plot_model
+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 = 100
+
+def encode_one_hot(s):
+    all = []
+    for c in s:
+        if c not in characters:
+            continue
+        x = np.zeros((INPUT_VOCAB_SIZE)) 
+        index = char_indices[c]
+        x[index] = 1 
+        all.append(x)
+    return all
+
+def decode_one_hot(x):
+    s = []
+    for onehot in x:
+        one_index = np.where(onehot == 1) # tuple of two things
+        if len(one_index[1]) > 0:
+            n = one_index[1][0]
+            c = indices_char[n]
+            s.append(c) 
+    return ''.join(s)
+    
+def normalization_layer_set_weights(n_layer):
+    wb = []
+    b = np.zeros((INPUT_VOCAB_SIZE), dtype=np.float32)
+    w = np.zeros((INPUT_VOCAB_SIZE, INPUT_VOCAB_SIZE), dtype=np.float32)
+    # Let lower case letters go through
+    for c in string.ascii_lowercase:
+        i = char_indices[c]
+        w[i, i] = 1
+    # Map capitals to lower case
+    for c in string.ascii_uppercase:
+        i = char_indices[c]
+        il = char_indices[c.lower()]
+        w[i, 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[i, sp_idx] = 1
+
+    wb.append(w)
+    wb.append(b)
+    n_layer.set_weights(wb)
+    return n_layer
+
+def build_model():
+    # Normalize characters using a shared dense model
+    n_layer = Dense(INPUT_VOCAB_SIZE)
+    raw_inputs = []
+    normalized_outputs = []
+    for _ in range(0, LINE_SIZE):
+        input_char = Input(shape=(INPUT_VOCAB_SIZE, ))
+        filtered_char = n_layer(input_char)
+        raw_inputs.append(input_char)
+        normalized_outputs.append(filtered_char)
+    normalization_layer_set_weights(n_layer)
+
+    model = Model(inputs=raw_inputs, outputs=normalized_outputs)
+    return model
+
+model = build_model()
+
+with open(sys.argv[1]) as f:
+    for line in f:
+        if line.isspace(): continue
+        onehots = encode_one_hot(line)
+
+        data = [[] for _ in range(LINE_SIZE)]
+        for i, c in enumerate(onehots):
+            data[i].append(c)
+        for j in range(len(onehots), LINE_SIZE):
+            data[j].append(np.zeros((INPUT_VOCAB_SIZE)))
+
+        inputs = [np.array(e) for e in data]
+        preds = model.predict(inputs)
+        normal = decode_one_hot(preds)
+
+        print(normal)