update

1 files changed, 106 insertions, 0 deletions

diff --git a/dlstuff/four.py b/dlstuff/four.py
new file mode 100755
index 0000000..983386a
--- /dev/null
+++ b/dlstuff/four.py
@@ -0,0 +1,106 @@
+#!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import numpy as np
+from keras.datasets import boston_housing
+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 build_model(train_data):
+    model = models.Sequential()
+    model.add(layers.Dense(64, activation="relu", input_shape=(train_data.shape[1],)))
+    model.add(layers.Dense(64, activation="relu"))
+    model.add(layers.Dense(1))
+    model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
+    return model
+
+def smooth_curve(points, factor=0.9):
+    smoothed_points = []
+    for point in points:
+        if smoothed_points:
+            previous = smoothed_points[-1]
+            smoothed_points.append(previous*factor+point*(1-factor))
+        else:
+            smoothed_points.append(point)
+    return smoothed_points
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    (train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
+    mean = train_data.mean(axis=0)
+    train_data -= mean
+    std = train_data.std(axis=0)
+    train_data /= std
+
+    test_data -= mean
+    test_data /= std
+
+    k = 4
+    num_epochs = 500
+    num_val_samples = len(train_data) // k
+    num_epochs = 100
+    all_scores = []
+    all_mae_histories = []
+
+    for i in range(k):
+        print("processing fold #", i)
+        val_data = train_data[i*num_val_samples:(i+1)*num_val_samples]
+        val_targets = train_targets[i*num_val_samples:(i+1)*num_val_samples]
+        partial_train_data = np.concatenate(
+                [train_data[:i*num_val_samples],
+                    train_data[(i+1)*num_val_samples:]], axis=0)
+        partial_train_targets = np.concatenate(
+                [train_targets[:i*num_val_samples],
+                    train_targets[(i+1)*num_val_samples:]], axis=0)
+        model = build_model(train_data)
+        history = model.fit(partial_train_data, partial_train_targets, validation_data=(val_data, val_targets), epochs=num_epochs, batch_size=1, verbose=0)
+        val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
+        mae_history = history.history["val_mean_absolute_error"]
+        all_mae_histories.append(mae_history)
+        all_scores.append(val_mae)
+
+    average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]
+    smoothed_mae_history = smooth_curve(average_mae_history[10:])
+    plt.plot(range(1, len(smoothed_mae_history) + 1), smoothed_mae_history)
+    plt.xlabel("Epochs")
+    plt.ylabel("Validation MAE")
+    plt.show()
+
+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()