inverse autoregressive flow optimizer test complete

scripts/flows/iaf/iaf_execute.py

+import os 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution() 
+import tensorflow_probability as tfp
+
+import matplotlib.pyplot as plt
+plt.style.use('seaborn')
+
+from data_loader import load_data
+from data_preprocesser import preprocess_data
+from maf import IAF
+
+def train(session, loss, optimizer, steps=int(1e5)):
+    
+    """ optimize for all dimensions """
+    
+    recorded_steps = []
+    recorded_losses = []
+    for i in range(steps):
+        _, loss_per_iteration = session.run([optimizer, loss])
+        if i % 100 == 0:
+            recorded_steps.append(i)
+            recorded_losses.append(loss_per_iteration)
+        if i % int(1e4) == 0:
+            print('Iteration {iteration}: {loss}'.format(iteration=i,loss=loss_per_iteration))
+    return recorded_losses
+
+def plot_results(recorded_losses):
+    
+    """ plot loss """
+    print('Displaying results...')
+    fig = plt.figure(figsize=(10,5))
+    x = np.arange(len(recorded_losses))
+    y = recorded_losses
+    m, b = np.polyfit(x, y, 1) 
+    plt.scatter(x, y, s=10, alpha=0.3)
+    plt.plot(x, m*x+b, c="r")
+    plt.title('Loss per 100 iteration')
+    plt.xlabel('Iteration')
+    plt.ylabel('Loss')
+    plt.tight_layout()
+    plt.show()
+
+def main():
+    
+    """ load data """
+
+    filename = 'prostate.xls'
+    directory = '/Users/kaanguney.keklikci/Data/'
+
+    loader = load_data(filename, directory)
+    loader.create_directory(directory)
+    data = loader.read_data(directory, filename)
+    print('Data successfully loaded...\n')
+    
+    """ preprocess data """
+
+    fillna_vals = ['sz', 'sg', 'wt']
+    dropna_vals = ['ekg', 'age']
+    drop_vals = ['patno', 'sdate']
+
+    preprocesser = preprocess_data(StandardScaler(), fillna_vals, dropna_vals, drop_vals)
+    data = preprocesser.dropna_features(data)
+    data = preprocesser.impute(data)
+    data = preprocesser.drop_features(data)
+    data = preprocesser.encode_categorical(data)
+    data = preprocesser.scale(data)
+    print('Data successfully preprocessed...\n')
+    
+    """ set MAF parameters """
+
+    batch_size = 32
+    dtype = np.float32
+    tf_version = tf.__version__
+    params = 2
+    hidden_units = [5,5]
+    base_dist = tfp.distributions.Normal(loc=0., scale=1.)
+    dims = data.shape[1]
+    learning_rate = 1e-4
+    activation = 'relu'
+    hidden_degrees = 'random'
+    conditional=True
+    conditional_event_shape = (dims,)
+    event_shape = conditional_event_shape
+    conditional_input_layers = 'first_layer'
+    
+    """ initialize samples """
+
+    iaf = IAF(dtype, tf_version, batch_size, 
+              params, hidden_units, base_dist, dims,
+              activation,
+              conditional, hidden_degrees, 
+              conditional_event_shape,
+              conditional_input_layers,
+              event_shape
+             )
+
+    dims = iaf.get_dims(data)
+    samples = iaf.create_tensor(data)
+    print(f'TensorFlow version: {iaf.tf_version}')
+    print(f'Number of dimensions: {iaf.dims}')
+    print(f'Learning rate: {learning_rate}\n')
+    
+    """ initialize iaf """
+
+    iaf = iaf.make_maf(data)
+    print('Successfully created model...\n')
+    
+    """ initialize loss and optimizer """
+
+    loss = -tf.reduce_mean(iaf.log_prob(samples, bijector_kwargs={'conditional_input': samples}))
+    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(loss)
+
+    session = tf.compat.v1.Session()
+    tf.compat.v1.set_random_seed(42)
+    session.run(tf.compat.v1.global_variables_initializer())
+    print('Optimizer and loss successfully defined...\n')
+    
+    """ start training """
+    recorded_losses = train(session, loss, optimizer)
+    print('Training finished...\n')
+    
+    """ display results """
+    plot_results(recorded_losses)
+    
+    
+if __name__ == "__main__":
+    main()
+

scripts/flows/iaf/iaf_optimizer_experiment.py

+""" use smaller learning rate for gradient descent or increase batch size """
+
+import os 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+import time 
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution() 
+import tensorflow_probability as tfp
+import tensorflow.python.util.deprecation as deprecation
+deprecation._PRINT_DEPRECATION_WARNINGS = False
+
+import matplotlib.pyplot as plt
+
+from data_loader import load_data
+from data_preprocesser import preprocess_data
+from maf import IAF 
+from experiment import Experiment
+
+def train(session, loss, optimizer, steps=int(1e5)):
+    
+    """ optimize for all dimensions """
+    
+    start_time = time.time()
+    
+    recorded_steps = []
+    recorded_losses = []
+    for i in range(steps):
+        _, loss_per_iteration = session.run([optimizer, loss])
+        if i % 100 == 0:
+            recorded_steps.append(i)
+            recorded_losses.append(loss_per_iteration)
+        if i % int(1e4) == 0:
+            print('Iteration {iteration}: {loss}'.format(iteration=i,loss=loss_per_iteration))
+    print('\nTraining completed...')
+    print(f'Training time: {time.time() - start_time} seconds')
+    return recorded_losses
+
+
+def plot_results(recorded_losses):
+    
+    """ plot loss """
+    print('Displaying results...')
+    fig = plt.figure(figsize=(10,5))
+    x = np.arange(len(recorded_losses))
+    y = recorded_losses
+    m, b = np.polyfit(x, y, 1) 
+    plt.scatter(x, y, s=10, alpha=0.3)
+    plt.plot(x, m*x+b, c="r")
+    plt.title('Loss per 100 iteration')
+    plt.xlabel('Iteration')
+    plt.ylabel('Loss')
+    plt.tight_layout()
+    plt.show()
+
+def main():
+    
+    """ load data """
+
+    filename = 'prostate.xls'
+    directory = '/Users/kaanguney.keklikci/Data/'
+
+    loader = load_data(filename, directory)
+    loader.create_directory(directory)
+    data = loader.read_data(directory, filename)
+    print('Data successfully loaded...\n')
+    
+    """ preprocess data """
+
+    fillna_vals = ['sz', 'sg', 'wt']
+    dropna_vals = ['ekg', 'age']
+    drop_vals = ['patno', 'sdate']
+
+    preprocesser = preprocess_data(StandardScaler(), fillna_vals, dropna_vals, drop_vals)
+    data = preprocesser.dropna_features(data)
+    data = preprocesser.impute(data)
+    data = preprocesser.drop_features(data)
+    data = preprocesser.encode_categorical(data)
+    data = preprocesser.scale(data)
+    print('Data successfully preprocessed...\n')
+    
+    """ set IAF parameters """
+
+    batch_size = 32
+    dtype = np.float32
+    tf_version = tf.__version__
+    params = 2
+    hidden_units = [5,5] # set this to a small number if you are using CPU
+    base_dist = tfp.distributions.Normal(loc=0., scale=1., name="gaussian")
+    dims = data.shape[1]
+    learning_rate = 1e-4
+    steps = 1e4
+    activation = 'relu'
+    hidden_degrees = 'random'
+    conditional=True
+    conditional_event_shape = (dims,)
+    event_shape = conditional_event_shape
+    conditional_input_layers = 'first_layer'
+    
+    """ initialize samples """
+
+    iaf = IAF(dtype, tf_version, batch_size, 
+              params, hidden_units, base_dist, dims,
+              activation,
+              conditional, hidden_degrees, 
+              conditional_event_shape,
+              conditional_input_layers,
+              event_shape
+             )
+
+    dims = iaf.get_dims(data)
+    samples = iaf.create_tensor(data)
+    print(f'TensorFlow version: {iaf.tf_version}')
+    print(f'Number of dimensions: {iaf.dims}')
+    print(f'Learning rate: {learning_rate}\n')
+    
+    """ initialize IAF """
+
+    iaf = iaf.make_maf(data)
+    print('Successfully created model...\n')
+    
+    """ initialize loss and optimizer """
+
+    loss = -tf.reduce_mean(iaf.log_prob(samples, bijector_kwargs={'conditional_input': samples}))    
+    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(loss)
+    
+    experiment = Experiment(optimizer, learning_rate, loss, steps)
+    
+    keywords = ['adam', 'rmsprop', 'sgd']
+    
+    for keyword in keywords:
+    
+        session = tf.compat.v1.Session()
+        tf.compat.v1.set_random_seed(42)
+        experiment.change_optimizer(learning_rate, loss, keyword=keyword)
+        optimizer = experiment.get_optimizer()
+        session.run(tf.compat.v1.global_variables_initializer())
+        print(f'Optimizer: {optimizer.name}')
+        print('Optimizer and loss successfully defined...\n')
+
+        """ start training """
+        recorded_losses = train(session, loss, optimizer)
+        print('Training finished...\n')
+
+        """ display results """
+        plot_results(recorded_losses)
+    
+    
+if __name__ == "__main__":
+    main()
+
+
+
+