# Set the random seed value np.random.seed(42) # Choose data and model size num_patterns = 100 data_dim = 200 # Get dataset train_data = np.float64( np.random.random_integers(0, 1, (num_patterns, data_dim))) train_label = np.float64( np.random.random_integers(0, 1, (num_patterns, data_dim))) # Choose activation function act = actFct.Sigmoid() # Train model, this performs a single trial as shown in Figure 8 a) in the Hebbina descent paper mymodel, results = train_Hebbian_descent_model(train_data=train_data, train_label=train_label, centered=True, act=actFct.Sigmoid(), epochs=1, epsilon=0.2, batch_size=1, weightdecay=0.0) # Display errors vis.xlabel("Pattern index (" + str(num_patterns) + " = latest pattern)") vis.ylabel("Mean Absolute Error")
# Split in tarining and test data train_data = data[0:50000] test_data = data[50000:70000] # Set hyperparameters batchsize and number of epochs batch_size = 10 max_epochs = 20 # Create model with sigmoid hidden units, linear output units, and squared error. ae = aeModel.AutoEncoder( v1 * v2, h1 * h2, data=train_data, visible_activation_function=act.Identity(), hidden_activation_function=act.Sigmoid(), cost_function=cost.SquaredError(), initial_weights=0.01, initial_visible_bias=0.0, initial_hidden_bias=-2.0, # Set initially the units to be inactive, speeds up learning a little bit initial_visible_offsets=0.0, initial_hidden_offsets=0.02, dtype=numx.float64) # Initialized gradient descent trainer trainer = aeTrainer.GDTrainer(ae) # Train model print 'Training' print 'Epoch\tRE train\t\tRE test\t\t\tSparsness train\t\tSparsness test '
h1 = 10 h2 = 10 # Load data , get it from 'deeplearning.net/data/mnist/mnist.pkl.gz' train_data, _, _, _, test_data, _ = io.load_mnist("../../data/mnist.pkl.gz", False) # Set hyperparameters batchsize and number of epochs batch_size = 10 max_epochs = 10 # Create model with sigmoid hidden units, linear output units, and squared error loss. ae = aeModel.AutoEncoder(v1 * v2, h1 * h2, data=train_data, visible_activation_function=act.Sigmoid(), hidden_activation_function=act.Sigmoid(), cost_function=cost.CrossEntropyError(), initial_weights='AUTO', initial_visible_bias='AUTO', initial_hidden_bias='AUTO', initial_visible_offsets='AUTO', initial_hidden_offsets='AUTO', dtype=numx.float64) # Initialized gradient descent trainer trainer = aeTrainer.GDTrainer(ae) # Train model print 'Training' print 'Epoch\tRE train\t\tRE test\t\t\tSparsness train\t\tSparsness test '