!pip install tensorflow==2.7.0

"""
Explanation: Overfit and Underfit
Learning objectives

Use the Higgs Dataset.
Demonstrate overfitting.
Strategies to prevent overfitting.

Introduction
In this notebook, we'll explore several common regularization techniques, and use them to improve on a classification model.
As always, the code in this example will use the tf.keras API, which you can learn more about in the TensorFlow Keras guide.
In both of the previous examples—classifying text and predicting fuel efficiency — we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then stagnate or start decreasing.
In other words, our model would overfit to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the training set, what we really want is to develop models that generalize well to a testing set (or data they haven't seen before).
The opposite of overfitting is underfitting. Underfitting occurs when there is still room for improvement on the train data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data.
If you train for too long though, the model will start to overfit and learn patterns from the training data that don't generalize to the test data. We need to strike a balance. Understanding how to train for an appropriate number of epochs as we'll explore below is a useful skill.
To prevent overfitting, the best solution is to use more complete training data. The dataset should cover the full range of inputs that the model is expected to handle. Additional data may only be useful if it covers new and interesting cases.
A model trained on more complete data will naturally generalize better. When that is no longer possible, the next best solution is to use techniques like regularization. These place constraints on the quantity and type of information your model can store.  If a network can only afford to memorize a small number of patterns, the optimization process will force it to focus on the most prominent patterns, which have a better chance of generalizing well.
Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.
Setup
Before getting started, import the necessary packages:
End of explanation
"""


import tensorflow as tf

from tensorflow.keras import layers
from tensorflow.keras import regularizers

print(tf.__version__)

!pip install git+https://github.com/tensorflow/docs

import tensorflow_docs as tfdocs
import tensorflow_docs.modeling
import tensorflow_docs.plots

from  IPython import display
from matplotlib import pyplot as plt

import numpy as np

import pathlib
import shutil
import tempfile


logdir = pathlib.Path(tempfile.mkdtemp())/"tensorboard_logs"
shutil.rmtree(logdir, ignore_errors=True)

"""
Explanation: NOTE: Please ignore any incompatibility warnings and errors.
NOTE: Restart your kernel to use updated packages.
End of explanation
"""


# TODO
# Downloads a file from a URL if it not already in the cache
gz = # TODO - Your code here ('HIGGS.csv.gz', 'http://mlphysics.ics.uci.edu/data/higgs/HIGGS.csv.gz')

FEATURES = 28

"""
Explanation: The Higgs Dataset
The goal of this tutorial is not to do particle physics, so don't dwell on the details of the dataset. It contains 11&#x202F;000&#x202F;000 examples, each with 28 features, and a binary class label.
End of explanation
"""


# A Dataset comprising lines from one or more CSV files.
ds = tf.data.experimental.CsvDataset(gz,[float(),]*(FEATURES+1), compression_type="GZIP")

"""
Explanation: The tf.data.experimental.CsvDataset class can be used to read csv records directly from a gzip file with no intermediate decompression step.
End of explanation
"""


def pack_row(*row):
  label = row[0]
  features = tf.stack(row[1:],1)
  return features, label

"""
Explanation: That csv reader class returns a list of scalars for each record. The following function repacks that list of scalars into a (feature_vector, label) pair.
End of explanation
"""


packed_ds = ds.batch(10000).map(pack_row).unbatch()

"""
Explanation: TensorFlow is most efficient when operating on large batches of data.
So instead of repacking each row individually make a new Dataset that takes batches of 10000-examples, applies the pack_row function to each batch, and then splits the batches back up into individual records:
End of explanation
"""


for features,label in packed_ds.batch(1000).take(1):
  print(features[0])
  plt.hist(features.numpy().flatten(), bins = 101)

"""
Explanation: Have a look at some of the records from this new packed_ds.
The features are not perfectly normalized, but this is sufficient for this tutorial.
End of explanation
"""


N_VALIDATION = int(1e3)
N_TRAIN = int(1e4)
BUFFER_SIZE = int(1e4)
BATCH_SIZE = 500
STEPS_PER_EPOCH = N_TRAIN//BATCH_SIZE

"""
Explanation: To keep this tutorial relatively short use just the first 1000 samples for validation, and the next 10 000 for training:
End of explanation
"""


# Creates a Dataset with at most count elements from this dataset.
# Creates a Dataset that skips count elements from this dataset.
validate_ds = packed_ds.take(N_VALIDATION).cache()
train_ds = packed_ds.skip(N_VALIDATION).take(N_TRAIN).cache()

train_ds

"""
Explanation: The Dataset.skip and Dataset.take methods make this easy.
At the same time, use the Dataset.cache method to ensure that the loader doesn't need to re-read the data from the file on each epoch:
End of explanation
"""


validate_ds = validate_ds.batch(BATCH_SIZE)
train_ds = train_ds.shuffle(BUFFER_SIZE).repeat().batch(BATCH_SIZE)

"""
Explanation: These datasets return individual examples. Use the .batch method to create batches of an appropriate size for training. Before batching also remember to .shuffle and .repeat the training set.
End of explanation
"""


# TODO
# A LearningRateSchedule that uses an inverse time decay schedule
lr_schedule = # TODO - Your code here(
  0.001,
  decay_steps=STEPS_PER_EPOCH*1000,
  decay_rate=1,
  staircase=False)

def get_optimizer():
  return tf.keras.optimizers.Adam(lr_schedule)

"""
Explanation: Demonstrate overfitting
The simplest way to prevent overfitting is to start with a small model: A model with a small number of learnable parameters (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.
Always keep this in mind: deep learning models tend to be good at fitting to the training data, but the real challenge is generalization, not fitting.
On the other hand, if the network has limited memorization resources, it will not be able to learn the mapping as easily. To minimize its loss, it will have to learn compressed representations that have more predictive power. At the same time, if you make your model too small, it will have difficulty fitting to the training data. There is a balance between "too much capacity" and "not enough capacity".
Unfortunately, there is no magical formula to determine the right size or architecture of your model (in terms of the number of layers, or the right size for each layer). You will have to experiment using a series of different architectures.
To find an appropriate model size, it's best to start with relatively few layers and parameters, then begin increasing the size of the layers or adding new layers until you see diminishing returns on the validation loss.
Start with a simple model using only layers.Dense as a baseline, then create larger versions, and compare them.
Training procedure
Many models train better if you gradually reduce the learning rate during training. Use optimizers.schedules to reduce the learning rate over time:
End of explanation
"""


step = np.linspace(0,100000)
lr = lr_schedule(step)
plt.figure(figsize = (8,6))
plt.plot(step/STEPS_PER_EPOCH, lr)
plt.ylim([0,max(plt.ylim())])
plt.xlabel('Epoch')
_ = plt.ylabel('Learning Rate')


"""
Explanation: The code above sets a schedules.InverseTimeDecay to hyperbolically decrease the learning rate to 1/2 of the base rate at 1000 epochs, 1/3 at 2000 epochs and so on.
End of explanation
"""


def get_callbacks(name):
  return [
    tfdocs.modeling.EpochDots(),
    tf.keras.callbacks.EarlyStopping(monitor='val_binary_crossentropy', patience=200),
    tf.keras.callbacks.TensorBoard(logdir/name),
  ]

"""
Explanation: Each model in this tutorial will use the same training configuration. So set these up in a reusable way, starting with the list of callbacks.
The training for this tutorial runs for many short epochs. To reduce the logging noise use the tfdocs.EpochDots which simply prints a . for each epoch, and a full set of metrics every 100 epochs.
Next include callbacks.EarlyStopping to avoid long and unnecessary training times. Note that this callback is set to monitor the val_binary_crossentropy, not the val_loss. This difference will be important later.
Use callbacks.TensorBoard to generate TensorBoard logs for the training.
End of explanation
"""


def compile_and_fit(model, name, optimizer=None, max_epochs=10000):
  if optimizer is None:
    optimizer = get_optimizer()
  model.compile(optimizer=optimizer,
                loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
                metrics=[
                  tf.keras.losses.BinaryCrossentropy(
                      from_logits=True, name='binary_crossentropy'),
                  'accuracy'])

  model.summary()

  history = model.fit(
    train_ds,
    steps_per_epoch = STEPS_PER_EPOCH,
    epochs=max_epochs,
    validation_data=validate_ds,
    callbacks=get_callbacks(name),
    verbose=0)
  return history

"""
Explanation: Similarly each model will use the same Model.compile and Model.fit settings:
End of explanation
"""


# Sequential groups a linear stack of layers into a tf.keras.Model
# TODO
tiny_model = # TODO - Your code here ([
    layers.Dense(16, activation='elu', input_shape=(FEATURES,)),
    layers.Dense(1)
])

size_histories = {}

size_histories['Tiny'] = compile_and_fit(tiny_model, 'sizes/Tiny')

"""
Explanation: Tiny model
Start by training a model:
End of explanation
"""


plotter = tfdocs.plots.HistoryPlotter(metric = 'binary_crossentropy', smoothing_std=10)
plotter.plot(size_histories)
plt.ylim([0.5, 0.7])

"""
Explanation: Now check how the model did:
End of explanation
"""


small_model = tf.keras.Sequential([
    # `input_shape` is only required here so that `.summary` works.
    layers.Dense(16, activation='elu', input_shape=(FEATURES,)),
    layers.Dense(16, activation='elu'),
    layers.Dense(1)
])

size_histories['Small'] = compile_and_fit(small_model, 'sizes/Small')

"""
Explanation: Small model
To see if you can beat the performance of the small model, progressively train some larger models.
Try two hidden layers with 16 units each:
End of explanation
"""


medium_model = tf.keras.Sequential([
    layers.Dense(64, activation='elu', input_shape=(FEATURES,)),
    layers.Dense(64, activation='elu'),
    layers.Dense(64, activation='elu'),
    layers.Dense(1)
])

"""
Explanation: Medium model
Now try 3 hidden layers with 64 units each:
End of explanation
"""


size_histories['Medium']  = compile_and_fit(medium_model, "sizes/Medium")

"""
Explanation: And train the model using the same data:
End of explanation
"""


large_model = tf.keras.Sequential([
    layers.Dense(512, activation='elu', input_shape=(FEATURES,)),
    layers.Dense(512, activation='elu'),
    layers.Dense(512, activation='elu'),
    layers.Dense(512, activation='elu'),
    layers.Dense(1)
])

"""
Explanation: Large model
As an exercise, you can create an even larger model, and see how quickly it begins overfitting.  Next, let's add to this benchmark a network that has much more capacity, far more than the problem would warrant:
End of explanation
"""


size_histories['large'] = compile_and_fit(large_model, "sizes/large")

"""
Explanation: And, again, train the model using the same data:
End of explanation
"""


plotter.plot(size_histories)
a = plt.xscale('log')
plt.xlim([5, max(plt.xlim())])
plt.ylim([0.5, 0.7])
plt.xlabel("Epochs [Log Scale]")

"""
Explanation: Plot the training and validation losses
The solid lines show the training loss, and the dashed lines show the validation loss (remember: a lower validation loss indicates a better model).
While building a larger model gives it more power, if this power is not constrained somehow it can easily overfit to the training set.
In this example, typically, only the "Tiny" model manages to avoid overfitting altogether, and each of the larger models overfit the data more quickly. This becomes so severe for the "large" model that you need to switch the plot to a log-scale to really see what's happening.
This is apparent if you plot and compare the validation metrics to the training metrics.

It's normal for there to be a small difference.
If both metrics are moving in the same direction, everything is fine.
If the validation metric begins to stagnate while the training metric continues to improve, you are probably close to overfitting.
If the validation metric is going in the wrong direction, the model is clearly overfitting.
End of explanation
"""


shutil.rmtree(logdir/'regularizers/Tiny', ignore_errors=True)
shutil.copytree(logdir/'sizes/Tiny', logdir/'regularizers/Tiny')

regularizer_histories = {}
regularizer_histories['Tiny'] = size_histories['Tiny']

"""
Explanation: Note: All the above training runs used the callbacks.EarlyStopping to end the training once it was clear the model was not making progress.
Strategies to prevent overfitting
Before getting into the content of this section copy the training logs from the "Tiny" model above, to use as a baseline for comparison.
End of explanation
"""


l2_model = tf.keras.Sequential([
    layers.Dense(512, activation='elu',
                 kernel_regularizer=regularizers.l2(0.001),
                 input_shape=(FEATURES,)),
    layers.Dense(512, activation='elu',
                 kernel_regularizer=regularizers.l2(0.001)),
    layers.Dense(512, activation='elu',
                 kernel_regularizer=regularizers.l2(0.001)),
    layers.Dense(512, activation='elu',
                 kernel_regularizer=regularizers.l2(0.001)),
    layers.Dense(1)
])

regularizer_histories['l2'] = compile_and_fit(l2_model, "regularizers/l2")

"""
Explanation: Add weight regularization
You may be familiar with Occam's Razor principle: given two explanations for something, the explanation most likely to be correct is the "simplest" one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some training data and a network architecture, there are multiple sets of weights values (multiple models) that could explain the data, and simpler models are less likely to overfit than complex ones.
A "simple model" in this context is a model where the distribution of parameter values has less entropy (or a model with fewer parameters altogether, as we saw in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity of a network by forcing its weights only to take small values, which makes the distribution of weight values more "regular". This is called "weight regularization", and it is done by adding to the loss function of the network a cost associated with having large weights. This cost comes in two flavors:


L1 regularization, where the cost added is proportional to the absolute value of the weights coefficients (i.e. to what is called the "L1 norm" of the weights).


L2 regularization, where the cost added is proportional to the square of the value of the weights coefficients (i.e. to what is called the squared "L2 norm" of the weights). L2 regularization is also called weight decay in the context of neural networks. Don't let the different name confuse you: weight decay is mathematically the exact same as L2 regularization.


L1 regularization pushes weights towards exactly zero encouraging a sparse model. L2 regularization will penalize the weights parameters without making them sparse since the penalty goes to zero for small weights-one reason why L2 is more common.
In tf.keras, weight regularization is added by passing weight regularizer instances to layers as keyword arguments. Let's add L2 weight regularization now.
End of explanation
"""


plotter.plot(regularizer_histories)
plt.ylim([0.5, 0.7])

"""
Explanation: l2(0.001) means that every coefficient in the weight matrix of the layer will add 0.001 * weight_coefficient_value**2 to the total loss of the network.
That is why we're monitoring the binary_crossentropy directly. Because it doesn't have this regularization component mixed in.
So, that same "Large" model with an L2 regularization penalty performs much better:
End of explanation
"""


result = l2_model(features)
regularization_loss=tf.add_n(l2_model.losses)

"""
Explanation: As you can see, the "L2" regularized model is now much more competitive with the the "Tiny" model. This "L2" model is also much more resistant to overfitting than the "Large" model it was based on despite having the same number of parameters.
More info
There are two important things to note about this sort of regularization.
First: if you are writing your own training loop, then you need to be sure to ask the model for its regularization losses.
End of explanation
"""


dropout_model = tf.keras.Sequential([
    layers.Dense(512, activation='elu', input_shape=(FEATURES,)),
    layers.Dropout(0.5),
    layers.Dense(512, activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(512, activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(512, activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(1)
])

regularizer_histories['dropout'] = compile_and_fit(dropout_model, "regularizers/dropout")

plotter.plot(regularizer_histories)
plt.ylim([0.5, 0.7])

"""
Explanation: Second: This implementation works by adding the weight penalties to the model's loss, and then applying a standard optimization procedure after that.
There is a second approach that instead only runs the optimizer on the raw loss, and then while applying the calculated step the optimizer also applies some weight decay. This "Decoupled Weight Decay" is seen in optimizers like optimizers.FTRL and optimizers.AdamW.
Add dropout
Dropout is one of the most effective and most commonly used regularization techniques for neural networks, developed by Hinton and his students at the University of Toronto.
The intuitive explanation for dropout is that because individual nodes in the network cannot rely on the output of the others, each node must output features that are useful on their own.
Dropout, applied to a layer, consists of randomly "dropping out" (i.e. set to zero) a number of output features of the layer during training. Let's say a given layer would normally have returned a vector [0.2, 0.5, 1.3, 0.8, 1.1] for a given input sample during training; after applying dropout, this vector will have a few zero entries distributed at random, e.g. [0, 0.5,
1.3, 0, 1.1].
The "dropout rate" is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test time, no units are dropped out, and instead the layer's output values are scaled down by a factor equal to the dropout rate, so as to balance for the fact that more units are active than at training time.
In tf.keras you can introduce dropout in a network via the Dropout layer, which gets applied to the output of layer right before.
Let's add two Dropout layers in our network to see how well they do at reducing overfitting:
End of explanation
"""


combined_model = tf.keras.Sequential([
    layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),
                 activation='elu', input_shape=(FEATURES,)),
    layers.Dropout(0.5),
    layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),
                 activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),
                 activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),
                 activation='elu'),
    layers.Dropout(0.5),
    layers.Dense(1)
])

regularizer_histories['combined'] = compile_and_fit(combined_model, "regularizers/combined")

# TODO
plotter.plot(# TODO - Your code here)
plt.ylim([0.5, 0.7])

"""
Explanation: It's clear from this plot that both of these regularization approaches improve the behavior of the "Large" model. But this still doesn't beat even the "Tiny" baseline.
Next try them both, together, and see if that does better.
Combined L2 + dropout
End of explanation
"""


