update

1 files changed, 84 insertions, 0 deletions

diff --git a/dlstuff/five.py b/dlstuff/five.py
new file mode 100755
index 0000000..0779e6c
--- /dev/null
+++ b/dlstuff/five.py
@@ -0,0 +1,84 @@
+#!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import keras
+from keras import Input, Model
+from keras import layers
+
+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
+    callbacks_list = [keras.callbacks.EarlyStopping(monitor="acc", patience=1,), keras.callbacks.ModelCheckpoint(filepath="mymodel.h5", monitor="val_loss", save_best_only=True,)]
+    input_tensor = Input(shape=(64,))
+    x = layers.Dense(32, activation="relu")(input_tensor)
+    x = layers.Dense(32, activation="relu")(x)
+    output_tensor = layers.Dense(10, activation="softmax")(x)
+    model = Model(input_tensor, output_tensor)
+    model.summary()
+    model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["acc"])
+    import numpy as np
+    x_train = np.random.random((1000, 64))
+    y_train = np.random.random((1000, 10))
+    x_val = np.random.random((1000, 64))
+    y_val = np.random.random((1000, 10))
+    model.fit(x_train, y_train, epochs=10, batch_size=128, callbacks=callbacks_list, validation_data=(x_val, y_val))
+    score = model.evaluate(x_train, y_train)
+    print(score)
+    '''
+    text_vocabulary_size = 10000
+    question_vocabulary_size = 10000
+    answer_vocabulary_size = 500
+    text_input = Input(shape=(None, ), dtype="int32", name="text")
+    embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input)
+    encoded_text = layers.LSTM(32)(embedded_text)
+    question_input = Input(shape=(None,), dtype="int32", name="question")
+    embedded_question = layers.Embedding(32, question_vocabulary_size)(question_input)
+    encoded_question = layers.LSTM(16)(embedded_question)
+    concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1)
+    answer = layers.Dense(answer_vocabulary_size, activatoin="softmax")(concatenated)
+    model = Model([text_input, question_input], answer)
+    model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["acc"])
+    import numpy as np
+    num_samples = 1000
+    max_length = 100
+    text = np.random.randint(1, text_vocabulary_size, size=(num_samples, max_length))
+    question = np.random.randint(1, question_vocabulary_size, size=(num_samples, max_length))
+    answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size))
+    model.fit([text, question], answers, epochs=10, batch_size=128)
+    '''
+
+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()