woods grantwwoodford
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| from keras.layers import Dense, Dropout | |
| from keras.models import Sequential | |
| import numpy as np | |
| import keras.backend as K | |
| def model(): | |
| # create model | |
| model = Sequential() | |
| model.add(Dense(100, input_dim=36, kernel_initializer='uniform', activation='relu')) |
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| from keras.layers import Dense, Dropout | |
| from keras.models import Sequential | |
| import numpy as np | |
| import keras.backend as K | |
| def model(): | |
| # create model | |
| model = Sequential() | |
| model.add(Dense(100, input_dim=36, kernel_initializer='uniform', activation='relu')) |
grantwwoodford
/ example.py
Created
March 20, 2017 18:49
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| # Create first network with Keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import numpy | |
| import threading as t | |
| import tensorflow as tf | |
| # create model | |
| model = Sequential() | |
| model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) |
grantwwoodford
/ hackyfix.py
Created
March 20, 2017 18:41
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| # Create first network with Keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import numpy | |
| import threading as t | |
| import tensorflow as tf | |
| graph = tf.get_default_graph() | |
| def t_thread(): |
grantwwoodford
/ problemcode.py
Last active
March 20, 2017 22:19
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| # Create first network with Keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import numpy | |
| import threading as t | |
| import tensorflow as tf | |
| def t_thread(): | |
| model = Sequential() | |
| model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) |
grantwwoodford
/ multithreading.py
Created
March 20, 2017 00:50
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| # Create first network with Keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import numpy | |
| import threading as t | |
| import tensorflow as tf | |
| graph = tf.get_default_graph() | |
| def t_thread(): |
grantwwoodford
/ crashexample.py
Created
March 19, 2017 23:59
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| # Create first network with Keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import numpy | |
| import thread | |
| def t_thread(): | |
| # create model | |
| model = Sequential() | |
| model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) |
grantwwoodford
/ RomanNumerals.fs
Created
February 16, 2016 20:26
Convert integers to Roman Numerals
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| let rec repeat x (s : string) : string = | |
| match x with | |
| | 0 -> "" | |
| | x -> s + (repeat (x-1) s) | |
| let roman_numeral_ordering n (one : string) (five : string) (ten : string) = | |
| match n with | |
| | 0 -> "" | |
| | n when n < 4 -> repeat n one | |
| | n when n = 4 -> one + five |