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test_list.py
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'''All optimizees used in the experiments.'''
import tensorflow as tf
import optimizee
tests = {
'mnist-nn-sigmoid-100': {
'frequency': 1,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid'),
'gd': lambda: tf.train.AdamOptimizer(0.0312500),
'n_steps': 100
},
'mnist-nn-sigmoid-2000': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid'),
'gd': lambda: tf.train.AdamOptimizer(0.0156250),
'n_steps': 2000
},
'mnist-nn-sigmoid-10000': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid'),
'gd': lambda: tf.train.AdamOptimizer(0.0078125),
'n_steps': 10000
},
'mnist-nn-relu-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='relu'),
'gd': lambda: tf.train.AdamOptimizer(0.0220971),
'n_steps': 100
},
'mnist-nn-elu-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='elu'),
'gd': lambda: tf.train.AdamOptimizer(0.0220971),
'n_steps': 100
},
'mnist-nn-tanh-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='tanh'),
'gd': lambda: tf.train.AdamOptimizer(0.0110485),
'n_steps': 100
},
'mnist-nn-l2-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=2),
'gd': lambda: tf.train.AdamOptimizer(0.0312500),
'n_steps': 100
},
'mnist-nn-l3-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=3),
'gd': lambda: tf.train.AdamOptimizer(0.0312500),
'n_steps': 100
},
'mnist-nn-l4-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=4),
'gd': lambda: tf.train.AdamOptimizer(0.0156250),
'n_steps': 100
},
'mnist-nn-l5-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=5),
'gd': lambda: tf.train.AdamOptimizer(0.0156250),
'n_steps': 100
},
'mnist-nn-l6-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=6),
'gd': lambda: tf.train.AdamOptimizer(0.0156250),
'n_steps': 100
},
'mnist-nn-l7-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=7),
'gd': lambda: tf.train.AdamOptimizer(0.0110485),
'n_steps': 100
},
'mnist-nn-l8-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=8),
'gd': lambda: tf.train.AdamOptimizer(0.0004883),
'n_steps': 100
},
'mnist-nn-l9-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=9),
'gd': lambda: tf.train.AdamOptimizer(0.0006905),
'n_steps': 100
},
'mnist-nn-l10-sigmoid-100': {
'frequency': 0,
'optimizee': optimizee.mnist.MnistLinearModel(activation='sigmoid', n_l=10),
'gd': lambda: tf.train.AdamOptimizer(0.0006905),
'n_steps': 100
},
'vgg-mnist-fc1-conv2-pool1-100': {
'frequency': 0,
'optimizee': optimizee.vgg.VGGModel(input_data='mnist', n_batches=128, fc_num=1, conv_num=2, pool_num=1),
'gd': lambda: tf.train.AdamOptimizer(0.0156250),
'n_steps': 100
},
'vgg-cifar-fc1-conv2-pool1-100': {
'frequency': 0,
'optimizee': optimizee.vgg.VGGModel(input_data='cifar10', n_batches=128, fc_num=1, conv_num=2, pool_num=1),
'gd': lambda: tf.train.AdamOptimizer(0.0078125),
'n_steps': 100
},
'vgg-mnist-fc2-conv4-pool2-100': {
'frequency': 0,
'optimizee': optimizee.vgg.VGGModel(input_data='mnist', n_batches=128, fc_num=2, conv_num=4, pool_num=2),
'gd': lambda: tf.train.AdamOptimizer(0.0055243),
'n_steps': 100
},
'vgg-cifar-fc2-conv4-pool2-100': {
'frequency': 0,
'optimizee': optimizee.vgg.VGGModel(input_data='cifar10', n_batches=128, fc_num=2, conv_num=4, pool_num=2),
'gd': lambda: tf.train.AdamOptimizer(0.0039062),
'n_steps': 100
},
'sin_lstm': {
'frequency': 0,
'optimizee': optimizee.lstm.SinLSTMModel(),
'gd': lambda: tf.train.AdagradOptimizer(0.5000000),
'n_steps': 100
},
'sin_lstm-x2': {
'frequency': 0,
'optimizee': optimizee.lstm.SinLSTMModel(n_lstm=2),
'gd': lambda: tf.train.AdamOptimizer(0.0220971),
'n_steps': 100
},
'sin_lstm-no001': {
'frequency': 0,
'optimizee': optimizee.lstm.SinLSTMModel(noise_scale=0.01),
'gd': lambda: tf.train.AdamOptimizer(0.0312500),
'n_steps': 100
}
}