diff --git a/35-dnn-no-learning/tf-35.py b/35-dnn-no-learning/tf-35.py
new file mode 100644
index 0000000..dfb6c5b
--- /dev/null
+++ b/35-dnn-no-learning/tf-35.py
@@ -0,0 +1,147 @@
+from keras.models import Model
+from keras import layers
+from keras.layers import Input, Dense
+from keras.utils import plot_model
+from keras import backend as K
+
+import numpy as np
+import sys, os, string
+
+characters = sorted(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
+MAX_WORD_SIZE = 20
+
+def encode_one_hot(s):
+    """One-hot encode all characters of the given string.
+    """
+    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):
+    """Return a string from a one-hot-encoded matrix
+    """
+    s = []
+    for onehot in x:
+        one_index = np.where(onehot == 1) # one_index is a 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 SpaceDetector(x):
+    print("x-sh", x.shape)
+#    print("input: ", K.eval(x))
+
+    sp_idx = char_indices[' ']
+    sp = np.zeros((INPUT_VOCAB_SIZE))
+    sp[sp_idx] = 1
+
+    filtered = x * sp
+#    print("filtered:", K.eval(filtered))
+    sp_positions = K.tf.where(K.tf.equal(filtered, 1)) # row indices
+    print(sp_positions.shape)
+#    print("sp-p:", K.eval(sp_positions))
+
+    starts = sp_positions[:-1] + [0, 1, 0]
+    stops = sp_positions[1:] + [0, 0, INPUT_VOCAB_SIZE]
+    sizes = stops - starts + [1, 0, 0]
+    where = K.tf.equal(sizes[:, 0], 1)
+    starts = K.tf.boolean_mask(starts, where) # Remove multi-sample rows
+    sizes = K.tf.boolean_mask(sizes, where) # Same
+    where = K.tf.greater(sizes[:, 1], 0)
+    starts = K.tf.boolean_mask(starts, where) # Remove words with 0 length (consecutive spaces)
+    sizes = K.tf.boolean_mask(sizes, where) # Same
+
+    print("starts:", starts, "sh:", starts.shape)
+    print("stops:", stops)
+    print("sizes:", sizes, "sh:", sizes.shape)
+
+    slices = K.map_fn(lambda info: K.tf.pad(K.squeeze(K.slice(x, info[0], info[1]), 0), [[0, MAX_WORD_SIZE - info[1][1]], [0,0]], "CONSTANT"), [starts, sizes], dtype=float)
+
+    return slices
+
+
+def build_model():
+    print('Build model...')
+    
+    # Normalize every character in the input, 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)
+
+    merged_output = layers.concatenate(normalized_outputs, axis=-1)
+
+    reshape = layers.Reshape((LINE_SIZE, INPUT_VOCAB_SIZE, ))
+    reshaped_output = reshape(merged_output)
+
+    # Find the space characters
+    words_output = layers.Lambda(SpaceDetector)(reshaped_output)
+
+    model = Model(inputs=raw_inputs, outputs=normalized_outputs)
+
+    return model
+
+model = build_model()
+#model.summary()
+plot_model(model, to_file='normalization.png', show_shapes=True)
+
+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(decode_one_hot(onehots))
+        print(normal)