1 files changed, 119 insertions, 0 deletions
diff --git a/dlstuff/three.py b/dlstuff/three.py
new file mode 100755
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+++ b/dlstuff/three.py
@@ -0,0 +1,119 @@
+#!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import numpy as np
+from keras.datasets import reuters
+from keras import models
+from keras import layers
+import matplotlib.pyplot as plt
+from keras.utils.np_utils import to_categorical
+
+def SigHandler_SIGINT(signum, frame):
+    print()
+    sys.exit(0)
+
+class Argparser(object):
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--string", type=str, help="string")
+        parser.add_argument("--bool", action="store_true", help="bool", default=False)
+        parser.add_argument("--dbg", action="store_true", help="debug", default=False)
+        self.args = parser.parse_args()
+
+def vectorize_sequences(sequences, dimension=10000):
+    results = np.zeros((len(sequences), dimension))
+    for i, sequence in enumerate(sequences):
+        results[i, sequence] = 1.
+    return results
+
+def to_one_hot(labels, dimension=46):
+    results = np.zeros((len(sequences), dimension))
+    for i, label in enumerate(labels):
+        results[i, label] = 1.
+    return results
+
+def plot_loss(history):
+    history_dic = history.history
+    loss_values = history_dic["loss"]
+    val_loss_values = history_dic["val_loss"]
+    epochs = range(1, len(history_dic["loss"]) + 1)
+    plt.plot(epochs, loss_values, "bo", label="Training Loss")
+    plt.plot(epochs, val_loss_values, "b", label="Validation Loss")
+    plt.title("training and validation loss")
+    plt.xlabel("Epochs")
+    plt.ylabel("Loss")
+    plt.legend()
+    plt.show()
+
+def plot_acc(history):
+    history_dic = history.history
+    acc_values = history_dic["acc"]
+    val_acc_values = history_dic["val_acc"]
+    epochs = range(1, len(history_dic["acc"]) + 1)
+    plt.plot(epochs, acc_values, "bo", label="Training Acc")
+    plt.plot(epochs, val_acc_values, "b", label="Validation Acc")
+    plt.title("training and validation acc")
+    plt.xlabel("Epochs")
+    plt.ylabel("Acc")
+    plt.legend()
+    plt.show()
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    (train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)
+    #print(len(train_data))
+    #print(len(test_data))
+    x_train = vectorize_sequences(train_data)
+    x_test = vectorize_sequences(test_data)
+    #one_hot_train_labels = to_one_hot(train_labels)
+    #one_hot_test_labels = to_one_hot(test_labels)
+    one_hot_train_labels = to_categorical(train_labels)
+    one_hot_test_labels = to_categorical(test_labels)
+
+    model = models.Sequential()
+    model.add(layers.Dense(64, activation="relu", input_shape=(10000,)))
+    #model.add(layers.Dense(64, activation="relu"))
+    model.add(layers.Dense(46, activation="softmax"))
+    model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
+
+    x_val = x_train[:1000]
+    partial_x_train = x_train[1000:]
+    y_val = one_hot_train_labels[:1000]
+    partial_y_train = one_hot_train_labels[1000:]
+    history = model.fit(partial_x_train, partial_y_train, epochs=9, batch_size=512, validation_data=(x_val, y_val))
+    '''
+    plot_loss(history)
+    plt.clf()
+    plot_acc(history)
+    '''
+    results = model.evaluate(x_test, one_hot_test_labels)
+    print(results)
+
+    predictions = model.predict(x_test)
+
+
+
+def main():
+    argparser = Argparser()
+    if argparser.args.dbg:
+        try:
+            premain(argparser)
+        except Exception as e:
+            print(e.__doc__)
+            if e.message: print(e.message)
+            variables = globals().copy()
+            variables.update(locals())
+            shell = code.InteractiveConsole(variables)
+            shell.interact(banner="DEBUG REPL")
+    else:
+        premain(argparser)
+
+if __name__ == "__main__":
+    main()