# Overfitting and underfitting

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

NUM_WORDS = 10000;

(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS);

def multi_hot_sequences(sequences, dimension):
    # Create an all-zero matrix of shape (len(sequences), dimension)
    results = np.zeros((len(sequences), dimension));
    for i, word_indices in enumerate(sequences):
        results[i, word_indices] = 1.0
    return results;

train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS);
test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS);

# The simplest way to prevent overfitting is to reduce the size of the model,
# i.e. the number of learnable parameters in the model (which is determined
# by the number of layers and the number of units per layer).
# In deep learning, the number of learnable parameters in a model is often
# referred to as the model's "capacity". Intuitively, a model with more
# parameters will have more "memorization capacity" and therefore will
# be able to easily learn a perfect dictionary-like mapping between training
# samples and their targets, a mapping without any generalization power, but
# this would be useless when making predictions on previously unseen data.

# Baseline model
baseline_model = keras.Sequential([
    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(16, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

baseline_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
baseline_model.summary();

baseline_history = baseline_model.fit(train_data,
                                      train_labels,
                                      epochs=20,
                                      batch_size=512,
                                      validation_data=(test_data, test_labels),
                                      verbose=2)

# Smaller model
smaller_model = keras.Sequential([
    keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(4, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

smaller_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
smaller_model.summary();

smaller_history = smaller_model.fit(train_data,
                                    train_labels,
                                    epochs=20,
                                    batch_size=512,
                                    validation_data=(test_data, test_labels),
                                    verbose=2)

# Bigger model
bigger_model = keras.Sequential([
    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(512, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

bigger_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
bigger_model.summary();

bigger_history = smaller_model.fit(train_data,
                                    train_labels,
                                    epochs=20,
                                    batch_size=512,
                                    validation_data=(test_data, test_labels),
                                    verbose=2)



def plot_history(histories, key='binary_crossentropy'):
    plt.figure(figsize=(16,10))

    for name, history in histories:
        val = plt.plot(history.epoch, history.history['val_'+key],
                   '--', label=name.title()+' Val')
        plt.plot(history.epoch, history.history[key], color=val[0].get_color(),
             label=name.title()+' Train')

    plt.xlabel('Epochs')
    plt.ylabel(key.replace('_',' ').title())
    plt.legend()

    plt.xlim([0,max(history.epoch)])
    plt.show();


plot_history([('baseline', baseline_history),
              ('smaller', smaller_history),
              ('bigger', bigger_history)])

# Overfitting and underfitting

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

NUM_WORDS = 10000;

(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS);

def multi_hot_sequences(sequences, dimension):
    # Create an all-zero matrix of shape (len(sequences), dimension)
    results = np.zeros((len(sequences), dimension));
    for i, word_indices in enumerate(sequences):
        results[i, word_indices] = 1.0
    return results;

train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS);
test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS);

# The simplest way to prevent overfitting is to reduce the size of the model,
# i.e. the number of learnable parameters in the model (which is determined
# by the number of layers and the number of units per layer).
# In deep learning, the number of learnable parameters in a model is often
# referred to as the model's "capacity". Intuitively, a model with more
# parameters will have more "memorization capacity" and therefore will
# be able to easily learn a perfect dictionary-like mapping between training
# samples and their targets, a mapping without any generalization power, but
# this would be useless when making predictions on previously unseen data.

# Baseline model
baseline_model = keras.Sequential([
    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(16, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

baseline_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
baseline_model.summary();

baseline_history = baseline_model.fit(train_data,
                                      train_labels,
                                      epochs=20,
                                      batch_size=512,
                                      validation_data=(test_data, test_labels),
                                      verbose=2)

# Smaller model
smaller_model = keras.Sequential([
    keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(4, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

smaller_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
smaller_model.summary();

smaller_history = smaller_model.fit(train_data,
                                    train_labels,
                                    epochs=20,
                                    batch_size=512,
                                    validation_data=(test_data, test_labels),
                                    verbose=2)

# Bigger model
bigger_model = keras.Sequential([
    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(10000,)),
    keras.layers.Dense(512, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.sigmoid)
]);

bigger_model.compile(optimizer='adam',
                       loss='binary_crossentropy',
                       metrics=['accuracy', 'binary_crossentropy']);
bigger_model.summary();

bigger_history = smaller_model.fit(train_data,
                                    train_labels,
                                    epochs=20,
                                    batch_size=512,
                                    validation_data=(test_data, test_labels),
                                    verbose=2)



def plot_history(histories, key='binary_crossentropy'):
    plt.figure(figsize=(16,10))

    for name, history in histories:
        val = plt.plot(history.epoch, history.history['val_'+key],
                   '--', label=name.title()+' Val')
        plt.plot(history.epoch, history.history[key], color=val[0].get_color(),
             label=name.title()+' Train')

    plt.xlabel('Epochs')
    plt.ylabel(key.replace('_',' ').title())
    plt.legend()

    plt.xlim([0,max(history.epoch)])
    plt.show();


plot_history([('baseline', baseline_history),
              ('smaller', smaller_history),
              ('bigger', bigger_history)])

# Overfitting and underfitting # Create an all-zero matrix of shape (len(sequences), dimension) # The simplest way to prevent overfitting is to reduce the size of the model, # i.e. the number of learnable parameters in the model (which is determined # by the number of layers and the number of units per layer). # In deep learning, the number of learnable parameters in a model is often # referred to as the model's "capacity". Intuitively, a model with more # parameters will have more "memorization capacity" and therefore will # be able to easily learn a perfect dictionary-like mapping between training # samples and their targets, a mapping without any generalization power, but # this would be useless when making predictions on previously unseen data. # Baseline model # Smaller model # Bigger model