Created
May 24, 2017 02:58
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import random | |
random.seed(a=1337) | |
k_fold = KFold(n_splits=10) | |
LABEL_COLUMN = "Ask.Low.Pc_H4_8AM" | |
resultz=[] | |
layerz=[] | |
stepz=[] | |
for i in range(100): | |
print("EXPERIMENT " + str(i)) | |
resultzz=[] | |
random.seed(a=1337+i) | |
num_layers = random.randint(2,5) | |
layer_sizes =[] | |
for j in range(num_layers): | |
layer_sizes.append(random.randint(2,1024)) | |
layerz.append(layer_sizes) | |
step = random.randint(10,1000) | |
stepz.append(step) | |
j = 0 | |
for train_indices, test_indices in k_fold.split(sellers): | |
model_dir = './models/m' + str(i) + "-" + str(j) | |
os.mkdir(model_dir) | |
def input_fn_train(): | |
return dict({k: tf.constant(sellers.iloc[train_indices][k].values, shape=[sellers.iloc[train_indices][k].size, 1]) | |
for k in CONTINUOUS_COLUMNS}), tf.constant(sellers.iloc[train_indices][LABEL_COLUMN].values) | |
def input_fn_eval(): | |
return dict({k: tf.constant(sellers.iloc[test_indices][k].values, shape=[sellers.iloc[test_indices][k].size, 1]) | |
for k in CONTINUOUS_COLUMNS}), tf.constant(sellers.iloc[test_indices][LABEL_COLUMN].values) | |
m=tf.contrib.learn.DNNRegressor(model_dir=model_dir,feature_columns=deep_columns, hidden_units=layer_sizes) | |
m.fit(input_fn=input_fn_train , steps=step) | |
time.sleep(1) | |
results = m.evaluate(input_fn=input_fn_eval, steps=1) | |
del m | |
gc.collect() | |
resultzz.append(results['loss']) | |
j = j+1 | |
resultz.append(resultzz) |
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