optimizer directory defined, planar experiments added

scripts/planar_optimizer_experiment.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
+from data_loader import load_data
+from data_preprocesser import preprocess_data
+from planar import Planar
+from experiment import Experiment
+import tensorflow.python.util.deprecation as deprecation
+deprecation._PRINT_DEPRECATION_WARNINGS = False
+
+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 create_tensor(data, batch_size):
+    dataset = tf.data.Dataset.from_tensor_slices(data.astype(np.float32))
+    dataset = dataset.repeat()
+    dataset = dataset.shuffle(buffer_size=data.shape[0])
+    dataset = dataset.prefetch(2*batch_size)
+    dataset = dataset.batch(batch_size)
+    data_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+    samples = data_iterator.get_next()
+    return samples
+
+""" 
+if any error on tensorflow is displayed claiming tf.float32 is not displayed,
+do the following (one of them is probably enough)
+    ** downgrade keras to 2.3.1
+    ** replace tf.float32 with np.float32
+"""
+def check_version():    
+    print(f'Tensorflow version: {tf.__version__}')
+    print(f'Tensorflow-probability version: {tfp.__version__}')
+    print(f'Keras version: {tf.keras.__version__}\n')
+
+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 Planar parameters """
+    
+    tfd = tfp.distributions
+    tfb = tfp.bijectors
+
+    batch_size = 32
+    dtype = np.float32
+    # layers should be higher but for comparison fix layers to 2 for every initial test
+    layers = 2
+    dims = data.shape[1]
+    # multivariate normal for base distribution
+    base_dist = tfd.MultivariateNormalDiag(loc=tf.zeros(shape=dims, dtype=dtype))
+    learning_rate = 1e-4
+    steps = int(1e4)
+    
+    """ initialize samples """
+    samples = create_tensor(data, batch_size)
+    
+    """ make Planar """
+
+    bijectors = []
+    for i in range(0, layers):
+        bijectors.append(Planar(input_dimensions=dims, case='density_estimation'))
+    bijector = tfb.Chain(bijectors=list(reversed(bijectors)), name='chain_of_planar')
+    planar_flow = tfd.TransformedDistribution(
+        distribution=base_dist,
+        bijector=bijector
+    )
+
+    loss = -tf.reduce_mean(planar_flow.log_prob(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()
+
+
+
+
+