weight_init_function_random
]
learning_rate_functions = [
None
] + [learning_rate_function] * n_hidden_layers + [learning_rate_function]
results = []
df_weights = DataFrame([])
df_netinputs = DataFrame([])
for seed_value in range(n_trials):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios,
weight_init_functions, learning_rate_functions)
network.set_neurons_drop_out_probabilities(neurons_drop_out_probabilities)
experiment_set_selected_weights(network)
####
# if disable_2nd_output_neuron:
# second_output_neuron = network.layers[-1].neurons[1]
# second_output_neuron.activation_function = ConstantOutput()
####
print "\n\nNet BEFORE Training\n", network
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, error_criterion,
# specify neuron transforms, weight initialization, and learning rate functions... per layer
neurons_ios = [None] + [hidden_neurons_io_function] * n_hidden_layers + [output_neurons_io_function]
weight_init_functions = [None] + [ weight_init_function_random ]*n_hidden_layers + [ weight_init_function_random ]
learning_rate_functions = [None] + [ learning_rate_function ]*n_hidden_layers + [ learning_rate_function ]
results = []
df_weights = DataFrame([])
df_netinputs = DataFrame([])
for seed_value in range(n_trials):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios, weight_init_functions, learning_rate_functions)
network.set_neurons_drop_out_probabilities(neurons_drop_out_probabilities)
experiment_set_selected_weights(network)
####
# if disable_2nd_output_neuron:
# second_output_neuron = network.layers[-1].neurons[1]
# second_output_neuron.activation_function = ConstantOutput()
####
print "\n\nNet BEFORE Training\n", network
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, error_criterion, max_epochs, data_collector) # sop call
nonmon = STDNonMonotonicIOFunction()
# specify neuron transforms, weight initialization, and learning rate functions... per layer
neurons_ios = [None] + [nonmon] * n_hidden_layers + [linear]
weight_init_functions = [None] + [ weight_init_function_random ]*n_hidden_layers + [ weight_init_function_random ]
learning_rate_functions = [None] + [ learning_rate_function ]*n_hidden_layers + [ learning_rate_function ]
results = []
dfs_concatenated = DataFrame([])
for seed_value in range(n_trials):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios, weight_init_functions, learning_rate_functions)
experiment_set_selected_weights(network)
print "\n\nNet BEFORE Training\n", network
data_collection_interval = 1000
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, error_criterion, max_epochs, data_collector) # sop call
# epoch_and_MSE = network.backpropagation(training_set, 0.0000001, max_epochs, data_collector)
results.append(epoch_and_MSE[0])
#print "\n\nNet After Training\n", network
# save the network
network.save_to_file( "trained_configuration.pkl" )
# specify neuron transforms, weight initialization, and learning rate functions... per layer
neurons_ios = [None] + [hidden_neurons_io_function] * n_hidden_layers + [output_neurons_io_function]
weight_init_functions = [None] + [ weight_init_function_random ]*n_hidden_layers + [ weight_init_function_random ]
learning_rate_functions = [None] + [ learning_rate_function ]*n_hidden_layers + [ learning_rate_function ]
results = []
df_weights = DataFrame([])
df_netinputs = DataFrame([])
for seed_value in range(n_trials):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios, weight_init_functions, learning_rate_functions)
experiment_set_selected_weights(network)
####
if disable_2nd_output_neuron:
second_output_neuron = network.layers[-1].neurons[1]
second_output_neuron.activation_function = ConstantOutput()
####
print "\n\nNet BEFORE Training\n", network
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, error_criterion, max_epochs, data_collector) # sop call
# epoch_and_MSE = network.backpropagation(training_set, 0.0000001, max_epochs, data_collector)
weight_init_function_random
] + [weight_init_function_random] * n_hidden_layers + [
weight_init_function_random
]
learning_rate_functions = [
learning_rate_function
] + [learning_rate_function] * n_hidden_layers + [learning_rate_function]
results = []
for seed_value in range(10):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios,
weight_init_functions, learning_rate_functions)
print "\n\nNetwork State just after creation\n", network
experimental_weight_setting_function(network)
data_collection_interval = 1000
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, 0.01, 3000,
data_collector)
results.append(epoch_and_MSE[0])
# save the network
network.save_to_file("trained_configuration.pkl")
n_hidden_layers = len(n_neurons_for_each_layer) - 2
# specify neuron transforms, weight initialization, and learning rate functions... per layer eg: [ hidden_layer_1, hidden_layer_2, output_layer ]
an_io_transform = SigmoidIO()
neurons_ios = [None] + [ an_io_transform ]*n_hidden_layers + [ an_io_transform ] # [ SigmoidIO() ] #
weight_init_functions = [ None ] + [ weight_init_function_random ]*n_hidden_layers + [ weight_init_function_random ]
learning_rate_functions = [ None ] + [ learning_rate_function ]*n_hidden_layers + [ learning_rate_function ]
results = []
for seed_value in range(10):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios, weight_init_functions, learning_rate_functions)
#print "\n\nNetwork State just after creation\n", network
data_collection_interval = 1000
data_collector = NetworkDataCollector(network, data_collection_interval)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, 0.01, 3000, data_collector)
results.append(epoch_and_MSE[0])
# save the network
network.save_to_file( "trained_configuration.pkl" )
# load a stored network
# network = NeuralNet.load_from_file( "trained_configuration.pkl" )
# specify neuron transforms, weight initialization, and learning rate functions... per layer
an_io_transform = STDNonMonotonicIOFunction()
neurons_ios = [an_io_transform] + [ an_io_transform ]*n_hidden_layers + [ an_io_transform ] # [ SigmoidIO() ] #
weight_init_functions = [ weight_init_function_random ] + [ weight_init_function_random ]*n_hidden_layers + [ weight_init_function_random ]
learning_rate_functions = [ learning_rate_function ] + [ learning_rate_function ]*n_hidden_layers + [ learning_rate_function ]
results = []
dfs_concatenated = DataFrame([])
for seed_value in range(10):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios, weight_init_functions, learning_rate_functions)
print "\n\nNetwork State just after creation\n", network
data_collector = NetworkDataCollector(network, data_collection_interval=1000)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set, error_limit=0.0000001, max_epochs=6000, data_collector=data_collector)
results.append(epoch_and_MSE[0])
dfs_concatenated = intermediate_post_process(seed_value, data_collector, dfs_concatenated)
# print out the result
for example_number, example in enumerate(training_set):
inputs_for_training_example = example.features
] + [weight_init_function_random] * n_hidden_layers + [
weight_init_function_random
]
learning_rate_functions = [
learning_rate_function
] + [learning_rate_function] * n_hidden_layers + [learning_rate_function]
results = []
dfs_concatenated = DataFrame([])
for seed_value in range(10):
print "seed = ", seed_value,
random.seed(seed_value)
# initialize the neural network
network = NeuralNet(n_neurons_for_each_layer, neurons_ios,
weight_init_functions, learning_rate_functions)
print "\n\nNetwork State just after creation\n", network
data_collector = NetworkDataCollector(network,
data_collection_interval=1000)
# start training on test set one
epoch_and_MSE = network.backpropagation(training_set,
error_limit=0.0000001,
max_epochs=6000,
data_collector=data_collector)
results.append(epoch_and_MSE[0])
dfs_concatenated = intermediate_post_process(seed_value, data_collector,
