Added 36

36-dense-shallow-out-of-control/tf-36-visualize-errors.py

@@ -0,0 +1,90 @@
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy as np
+import sys, os, string, random
+
+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)
+BATCH_SIZE = 200
+
+def encode_one_hot(line):
+    x = np.zeros((len(line), INPUT_VOCAB_SIZE))
+    for i, c in enumerate(line):
+        if c in characters:
+            index = char_indices[c]
+        else:
+            index = char_indices[' ']
+        x[i][index] = 1 
+    return x
+
+def decode_one_hot(x):
+    s = []
+    for onehot in x:
+        one_index = np.argmax(onehot) 
+        s.append(indices_char[one_index]) 
+    return ''.join(s)
+    
+def input_generator(nsamples):
+    def generate_line():
+        inline = []; outline = []
+        for _ in range(nsamples):
+            c = random.choice(characters) 
+            expected = c.lower() if c in string.ascii_letters else ' ' 
+            inline.append(c); outline.append(expected)
+        return ''.join(inline), ''.join(outline)
+
+    while True:
+        input_data, expected = generate_line()
+        data_in = encode_one_hot(input_data)
+        data_out = encode_one_hot(expected)
+        yield data_in, data_out
+
+def build_model():
+    # Normalize characters using a dense layer
+    model = Sequential()
+    dense_layer = Dense(INPUT_VOCAB_SIZE, 
+                        input_shape=(INPUT_VOCAB_SIZE,),
+                        activation='softmax')
+    model.add(dense_layer)
+    return model
+
+def train_model(model):
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='adam',
+                  metrics=['accuracy'])
+    # Train the model each generation and show predictions
+    val_gen2 = input_generator(20)
+    for iteration in range(1, 50):
+        print()
+        print('Iteration', iteration, '-' * 50)
+        input_gen = input_generator(BATCH_SIZE)
+        val_gen = input_generator(BATCH_SIZE)
+        model.fit_generator(input_gen,
+                    epochs = 1, workers=1,
+                    steps_per_epoch = 20,
+                    validation_data = val_gen,
+                    validation_steps = 10)
+        # Visualize errors
+        batch_x, batch_y = next(val_gen2)
+        preds = model.predict(batch_x)
+        original = decode_one_hot(batch_x)
+        correct = decode_one_hot(batch_y)
+        guess = decode_one_hot(preds)
+        print('Original   :', original)
+        print('True output:', correct)
+        print('Prediction :', guess)
+
+model = build_model()
+train_model(model)
+input("Network has been trained. Press <Enter> to run program.")
+
+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)

36-dense-shallow-out-of-control/tf-36.py

@@ -0,0 +1,77 @@
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy as np
+import sys, os, string, random
+
+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)
+BATCH_SIZE = 200
+
+def encode_one_hot(line):
+    x = np.zeros((len(line), INPUT_VOCAB_SIZE))
+    for i, c in enumerate(line):
+        if c in characters:
+            index = char_indices[c]
+        else:
+            index = char_indices[' ']
+        x[i][index] = 1 
+    return x
+
+def decode_one_hot(x):
+    s = []
+    for onehot in x:
+        one_index = np.argmax(onehot) 
+        s.append(indices_char[one_index]) 
+    return ''.join(s)
+    
+def build_model():
+    # Normalize characters using a dense layer
+    model = Sequential()
+    dense_layer = Dense(INPUT_VOCAB_SIZE, 
+                        input_shape=(INPUT_VOCAB_SIZE,),
+                        activation='softmax')
+    model.add(dense_layer)
+    return model
+
+def input_generator(nsamples):
+    def generate_line():
+        inline = []; outline = []
+        for _ in range(nsamples):
+            c = random.choice(characters) 
+            expected = c.lower() if c in string.ascii_letters else ' ' 
+            inline.append(c); outline.append(expected)
+        return ''.join(inline), ''.join(outline)
+
+    while True:
+        input_data, expected = generate_line()
+        data_in = encode_one_hot(input_data)
+        data_out = encode_one_hot(expected)
+        yield data_in, data_out
+
+def train(model):
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='adam',
+                  metrics=['accuracy'])
+    input_gen = input_generator(BATCH_SIZE)
+    validation_gen = input_generator(BATCH_SIZE)
+    model.fit_generator(input_gen,
+                epochs = 50, workers=1,
+                steps_per_epoch = 20,
+                validation_data = validation_gen,
+                validation_steps = 10)
+
+model = build_model()
+model.summary()
+train(model)
+
+input("Network has been trained. Press <Enter> to run program.")
+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)