1 files changed, 78 insertions, 0 deletions
diff --git a/dlstuff/one.py b/dlstuff/one.py
new file mode 100755
index 0000000..4012b89
--- /dev/null
+++ b/dlstuff/one.py
@@ -0,0 +1,78 @@
+#!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+from keras.datasets import mnist
+from keras import models
+from keras import layers
+from keras.utils import to_categorical
+import matplotlib.pyplot as plt
+
+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()
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+    '''
+    print(train_images.shape)
+    print(len(train_labels))
+    print(train_labels)
+    print(test_images.shape)
+    print(len(test_labels))
+    print(test_labels)
+    digit = train_images[4]
+    plt.imshow(digit, cmap=plt.cm.binary)
+    plt.show()
+    '''
+
+    network = models.Sequential()
+    network.add(layers.Dense(512, activation="relu", input_shape=(28*28,)))
+    network.add(layers.Dense(10, activation="softmax"))
+    #network.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
+    network.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
+
+    train_images = train_images.reshape((60000, 28 * 28))
+    train_images = train_images.astype("float32") / 255
+    test_images = test_images.reshape((10000, 28 * 28))
+    test_images = test_images.astype("float32") / 255
+    train_labels = to_categorical(train_labels)
+    test_labels = to_categorical(test_labels)
+
+    network.fit(train_images, train_labels, epochs=5, batch_size=128)
+
+    test_loss, test_acc = network.evaluate(test_images, test_labels)
+    print("test_acc:", test_acc)
+
+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()