add version of experiments that uses the parameters chosen in paper i…

…nstead of doing parameter search

DiscreteSpectrumExampleExperimentBestParams.py

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+import copy
+
+import training
+
+params = {}
+
+# settings related to dataset
+params['data_name'] = 'DiscreteSpectrumExample'
+params['data_train_len'] = 1
+params['len_time'] = 51
+n = 2  # dimension of system (and input layer)
+num_initial_conditions = 5000  # per training file
+params['delta_t'] = 0.02
+
+# settings related to saving results
+params['folder_name'] = 'exp1_best'
+
+# settings related to network architecture
+params['num_real'] = 2
+params['num_complex_pairs'] = 0
+params['num_evals'] = 2
+k = params['num_evals']  # dimension of y-coordinates
+w = 30
+params['widths'] = [2, w, w, k, k, w, w, 2]
+wo = 10
+params['hidden_widths_omega'] = [wo, wo, wo]
+
+# settings related to loss function
+params['num_shifts'] = 30
+params['num_shifts_middle'] = params['len_time'] - 1
+max_shifts = max(params['num_shifts'], params['num_shifts_middle'])
+num_examples = num_initial_conditions * (params['len_time'] - max_shifts)
+params['recon_lam'] = .1
+params['Linf_lam'] = 10 ** (-7)
+params['L1_lam'] = 0.0
+params['L2_lam'] = 10 ** (-15)
+params['auto_first'] = 0
+
+# settings related to the training
+params['num_passes_per_file'] = 15 * 6 * 10
+params['num_steps_per_batch'] = 2
+params['learning_rate'] = 10 ** (-3)
+params['batch_size'] = 256
+steps_to_see_all = num_examples / params['batch_size']
+params['num_steps_per_file_pass'] = (int(steps_to_see_all) + 1) * params['num_steps_per_batch']
+
+# settings related to the timing
+params['max_time'] = 4 * 60 * 60  # 4 hours
+params['min_5min'] = .5
+params['min_20min'] = .0004
+params['min_40min'] = .00008
+params['min_1hr'] = .00003
+params['min_2hr'] = .00001
+params['min_3hr'] = .000006
+params['min_halfway'] = .000006
+
+for count in range(200):  # loop to do random experiments
+    training.main_exp(copy.deepcopy(params))

FluidFlowBoxExperimentBestParams.py

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+import copy
+
+import training
+
+params = {}
+
+# settings related to dataset
+params['data_name'] = 'FluidFlowBox'
+params['data_train_len'] = 4
+params['len_time'] = 101
+n = 3  # dimension of system (and input layer)
+num_initial_conditions = 5000  # per training file
+params['delta_t'] = 0.01
+
+# settings related to saving results
+params['folder_name'] = 'exp4_best'
+
+# settings related to network architecture
+params['num_real'] = 1
+params['num_complex_pairs'] = 1
+params['num_evals'] = 3
+k = params['num_evals']  # dimension of y-coordinates
+w = 130
+params['widths'] = [3, w, k, k, w, 3]
+wo = 20
+params['hidden_widths_omega'] = [wo, wo]
+
+# defaults related to initialization of parameters
+params['dist_weights'] = 'dl'
+params['dist_weights_omega'] = 'dl'
+
+# settings related to loss function
+params['num_shifts'] = 30
+params['num_shifts_middle'] = params['len_time'] - 1
+max_shifts = max(params['num_shifts'], params['num_shifts_middle'])
+num_examples = num_initial_conditions * (params['len_time'] - max_shifts)
+params['recon_lam'] = .1
+params['Linf_lam'] = 10 ** (-9)
+params['L1_lam'] = 0.0
+params['L2_lam'] = 10 ** (-13)
+params['auto_first'] = 1
+
+# settings related to training
+params['num_passes_per_file'] = 15 * 6 * 10
+params['num_steps_per_batch'] = 2
+params['learning_rate'] = 10 ** (-3)
+params['batch_size'] = 128
+steps_to_see_all = num_examples / params['batch_size']
+params['num_steps_per_file_pass'] = (int(steps_to_see_all) + 1) * params['num_steps_per_batch']
+
+# settings related to timing
+params['max_time'] = 6 * 60 * 60  # 6 hours
+params['min_5min'] = .45
+params['min_20min'] = .005
+params['min_40min'] = .0005
+params['min_1hr'] = .00025
+params['min_2hr'] = .00005
+params['min_3hr'] = .000007
+params['min_4hr'] = .000005
+params['min_halfway'] = 1
+
+for count in range(200):  # loop to do random experiments
+    training.main_exp(copy.deepcopy(params))

FluidFlowOnAttractorExperimentBestParams.py

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+import copy
+
+import training
+
+params = {}
+
+# settings related to dataset
+params['data_name'] = 'FluidFlowOnAttractor'
+params['data_train_len'] = 3
+params['len_time'] = 121
+n = 3  # dimension of system (and input layer)
+num_initial_conditions = 5000  # per training file
+params['delta_t'] = 0.05
+
+# settings related to saving results
+params['folder_name'] = 'exp3_best'
+
+# settings related to network architecture
+params['num_real'] = 0
+params['num_complex_pairs'] = 1
+params['num_evals'] = 2
+k = params['num_evals']  # dimension of y-coordinates
+w = 105
+params['widths'] = [3, w, k, k, w, 3]
+wo = 300
+params['hidden_widths_omega'] = [wo, ]
+
+# settings related to loss function
+params['num_shifts'] = 30
+params['num_shifts_middle'] = params['len_time'] - 1
+max_shifts = max(params['num_shifts'], params['num_shifts_middle'])
+num_examples = num_initial_conditions * (params['len_time'] - max_shifts)
+params['recon_lam'] = .1
+params['Linf_lam'] = 10 ** (-7)
+params['L1_lam'] = 0.0
+params['L2_lam'] = 10 ** (-13)
+params['auto_first'] = 1
+
+# settings related to training
+params['num_passes_per_file'] = 15 * 6 * 10
+params['num_steps_per_batch'] = 2
+params['learning_rate'] = 10 ** (-3)
+params['batch_size'] = 256
+steps_to_see_all = num_examples / params['batch_size']
+params['num_steps_per_file_pass'] = (int(steps_to_see_all) + 1) * params['num_steps_per_batch']
+
+# settings related to timing
+params['max_time'] = 6 * 60 * 60  # 6 hours
+params['min_5min'] = .45
+params['min_20min'] = .001
+params['min_40min'] = .0005
+params['min_1hr'] = .00025
+params['min_2hr'] = .00005
+params['min_3hr'] = .000005
+params['min_4hr'] = .0000007
+params['min_halfway'] = 1
+
+for count in range(200):  # loop to do random experiments
+    training.main_exp(copy.deepcopy(params))

PendulumExperimentBestParams.py

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+import copy
+
+import training
+
+params = {}
+
+# settings related to dataset
+params['data_name'] = 'Pendulum'
+params['data_train_len'] = 3
+params['len_time'] = 51
+n = 2  # dimension of system (and input layer)
+num_initial_conditions = 5000  # per training file
+params['delta_t'] = 0.02
+
+# settings related to saving results
+params['folder_name'] = 'exp2_best'
+
+# settings related to network architecture
+params['num_real'] = 0
+params['num_complex_pairs'] = 1
+params['num_evals'] = 2
+k = params['num_evals']  # dimension of y-coordinates
+w = 80
+params['widths'] = [2, w, w, k, k, w, w, 2]
+wo = 170
+params['hidden_widths_omega'] = [wo, ]
+
+# defaults related to initialization of parameters
+params['dist_weights'] = 'dl'
+params['dist_weights_omega'] = 'dl'
+
+# settings related to loss function
+params['num_shifts'] = 30
+params['num_shifts_middle'] = params['len_time'] - 1
+max_shifts = max(params['num_shifts'], params['num_shifts_middle'])
+num_examples = num_initial_conditions * (params['len_time'] - max_shifts)
+params['recon_lam'] = .001
+params['Linf_lam'] = 10 ** (-9)
+params['L1_lam'] = 0.0
+params['L2_lam'] = 10 ** (-14)
+params['auto_first'] = 1
+
+# settings related to training
+params['num_passes_per_file'] = 15 * 6 * 50
+params['num_steps_per_batch'] = 2
+params['learning_rate'] = 10 ** (-3)
+params['batch_size'] = 128
+steps_to_see_all = num_examples / params['batch_size']
+params['num_steps_per_file_pass'] = (int(steps_to_see_all) + 1) * params['num_steps_per_batch']
+
+# settings related to timing
+params['max_time'] = 6 * 60 * 60  # 6 hours
+params['min_5min'] = .25
+params['min_20min'] = .02
+params['min_40min'] = .002
+params['min_1hr'] = .0002
+params['min_2hr'] = .00002
+params['min_3hr'] = .000004
+params['min_4hr'] = .0000005
+params['min_halfway'] = 1
+
+for count in range(200):  # loop to do random experiments
+    training.main_exp(copy.deepcopy(params))