Develop Reference

Custom Neural Network Implementation on MNIST using Tensorflow 2.0?

I tried to write a custom implementation of basic neural network with two hidden layers on MNIST dataset using *TensorFlow 2.0 beta* but I'm not sure what went wrong here but my training loss and accuracy seems to stuck at 1.5 and around 85 respectively. But If I build the using Keras I was getting very low training loss and accuracy above 95% with just 8-10 epochs.
I believe that maybe I'm not updating my weights or something? So do I need to assign my new weights which I compute in backprop function backs to their respective weights/bias variables?
I really appreciate if someone could help me out with this and these few more questions that I've mentioned below.
Few more Questions:
1) How to add a Dropout and Batch Normalization layer in this custom implementation? (i.e making it work for both train and test time)
2) How can I use callbacks in this code? i.e (making use of EarlyStopping and ModelCheckpoint callbacks)
3) Is there anything else in my code below that I can optimize further in this code like maybe making use of tensorflow 2.x #tf.function decorator etc.)
4) I would also require to extract the final weights that I obtain for plotting and checking their distributions. To investigate issues like gradient vanishing or exploding. (Eg: Maybe Tensorboard)
5) I also want help in writing this code in a more generalized way so I can easily implement other networks like ConvNets (i.e Conv, MaxPool, etc.) based on this code easily.
Here's my full code for easy reproducibility :
Note: I know I can use high-level API like Keras to build the model much easier but that is not my goal here. Please understand.
import numpy as np
import os
import logging
logging.getLogger('tensorflow').setLevel(logging.ERROR)
import tensorflow as tf
import tensorflow_datasets as tfds
(x_train, y_train), (x_test, y_test) = tfds.load('mnist', split=['train', 'test'],
batch_size=-1, as_supervised=True)
# reshaping
x_train = tf.reshape(x_train, shape=(x_train.shape[0], 784))
x_test = tf.reshape(x_test, shape=(x_test.shape[0], 784))
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
# rescaling
ds_train = ds_train.map(lambda x, y: (tf.cast(x, tf.float32)/255.0, y))
class Model(object):
def __init__(self, hidden1_size, hidden2_size, device=None):
# layer sizes along with input and output
self.input_size, self.output_size, self.device = 784, 10, device
self.hidden1_size, self.hidden2_size = hidden1_size, hidden2_size
self.lr_rate = 1e-03
# weights initializationg
self.glorot_init = tf.initializers.glorot_uniform(seed=42)
# weights b/w input to hidden1 --> 1
self.w_h1 = tf.Variable(self.glorot_init((self.input_size, self.hidden1_size)))
# weights b/w hidden1 to hidden2 ---> 2
self.w_h2 = tf.Variable(self.glorot_init((self.hidden1_size, self.hidden2_size)))
# weights b/w hidden2 to output ---> 3
self.w_out = tf.Variable(self.glorot_init((self.hidden2_size, self.output_size)))
# bias initialization
self.b1 = tf.Variable(self.glorot_init((self.hidden1_size,)))
self.b2 = tf.Variable(self.glorot_init((self.hidden2_size,)))
self.b_out = tf.Variable(self.glorot_init((self.output_size,)))
self.variables = [self.w_h1, self.b1, self.w_h2, self.b2, self.w_out, self.b_out]
def feed_forward(self, x):
if self.device is not None:
with tf.device('gpu:0' if self.device=='gpu' else 'cpu'):
# layer1
self.layer1 = tf.nn.sigmoid(tf.add(tf.matmul(x, self.w_h1), self.b1))
# layer2
self.layer2 = tf.nn.sigmoid(tf.add(tf.matmul(self.layer1,
self.w_h2), self.b2))
# output layer
self.output = tf.nn.softmax(tf.add(tf.matmul(self.layer2,
self.w_out), self.b_out))
return self.output
def loss_fn(self, y_pred, y_true):
self.loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,
logits=y_pred)
return tf.reduce_mean(self.loss)
def acc_fn(self, y_pred, y_true):
y_pred = tf.cast(tf.argmax(y_pred, axis=1), tf.int32)
y_true = tf.cast(y_true, tf.int32)
predictions = tf.cast(tf.equal(y_true, y_pred), tf.float32)
return tf.reduce_mean(predictions)
def backward_prop(self, batch_xs, batch_ys):
optimizer = tf.keras.optimizers.Adam(learning_rate=self.lr_rate)
with tf.GradientTape() as tape:
predicted = self.feed_forward(batch_xs)
step_loss = self.loss_fn(predicted, batch_ys)
grads = tape.gradient(step_loss, self.variables)
optimizer.apply_gradients(zip(grads, self.variables))
n_shape = x_train.shape[0]
epochs = 20
batch_size = 128
ds_train = ds_train.repeat().shuffle(n_shape).batch(batch_size).prefetch(batch_size)
neural_net = Model(512, 256, 'gpu')
for epoch in range(epochs):
no_steps = n_shape//batch_size
avg_loss = 0.
avg_acc = 0.
for (batch_xs, batch_ys) in ds_train.take(no_steps):
preds = neural_net.feed_forward(batch_xs)
avg_loss += float(neural_net.loss_fn(preds, batch_ys)/no_steps)
avg_acc += float(neural_net.acc_fn(preds, batch_ys) /no_steps)
neural_net.backward_prop(batch_xs, batch_ys)
print(f'Epoch: {epoch}, Training Loss: {avg_loss}, Training ACC: {avg_acc}')
# output for 10 epochs:
Epoch: 0, Training Loss: 1.7005115111824125, Training ACC: 0.7603832868262543
Epoch: 1, Training Loss: 1.6052448933478445, Training ACC: 0.8524806404020637
Epoch: 2, Training Loss: 1.5905528008006513, Training ACC: 0.8664196092868224
Epoch: 3, Training Loss: 1.584107405738905, Training ACC: 0.8727630912326276
Epoch: 4, Training Loss: 1.5792385798413306, Training ACC: 0.8773203844903037
Epoch: 5, Training Loss: 1.5759121985174716, Training ACC: 0.8804754322627559
Epoch: 6, Training Loss: 1.5739163148682564, Training ACC: 0.8826455712551251
Epoch: 7, Training Loss: 1.5722616605926305, Training ACC: 0.8840812018606812
Epoch: 8, Training Loss: 1.569699136307463, Training ACC: 0.8867688354803249
Epoch: 9, Training Loss: 1.5679460542742163, Training ACC: 0.8885049475356936

I wondered where to start with your multiquestion, and I decided to do so with a statement:
Your code definitely should not look like that and is nowhere near current Tensorflow best practices.
Sorry, but debugging it step by step is waste of everyone's time and would not benefit either of us.
Now, moving to the third point:
Is there anything else in my code below that I can optimize further
in this code like maybe making use of tensorflow 2.x #tf.function
decorator etc.)
Yes, you can use tensorflow2.0 functionalities and it seems like you are running away from those (tf.function decorator is of no use here actually, leave it for the time being).
Following new guidelines would alleviate your problems with your 5th point as well, namely:
I also want help in writing this code in a more generalized way so
I can easily implement other networks like ConvNets (i.e Conv, MaxPool
etc.) based on this code easily.
as it's designed specifically for that. After a little introduction I will try to introduce you to those concepts in a few steps:
1. Divide your program into logical parts
Tensorflow did much harm when it comes to code readability; everything in tf1.x was usually crunched in one place, globals followed by function definition followed by another globals or maybe data loading, all in all mess. It's not really developers fault as the system's design encouraged those actions.
Now, in tf2.0 programmer is encouraged to divide his work similarly to the structure one can see in pytorch, chainer and other more user-friendly frameworks.
1.1 Data loading
You were on good path with Tensorflow Datasets but you turned away for no apparent reason.
Here is your code with commentary what's going on:
# You already have tf.data.Dataset objects after load
(x_train, y_train), (x_test, y_test) = tfds.load('mnist', split=['train', 'test'],
batch_size=-1, as_supervised=True)
# But you are reshaping them in a strange manner...
x_train = tf.reshape(x_train, shape=(x_train.shape[0], 784))
x_test = tf.reshape(x_test, shape=(x_test.shape[0], 784))
# And building from slices...
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
# Unreadable rescaling (there are built-ins for that)
You can easily generalize this idea for any dataset, place this in separate module, say datasets.py:
import tensorflow as tf
import tensorflow_datasets as tfds
class ImageDatasetCreator:
#classmethod
# More portable and readable than dividing by 255
def _convert_image_dtype(cls, dataset):
return dataset.map(
lambda image, label: (
tf.image.convert_image_dtype(image, tf.float32),
label,
)
)
def __init__(self, name: str, batch: int, cache: bool = True, split=None):
# Load dataset, every dataset has default train, test split
dataset = tfds.load(name, as_supervised=True, split=split)
# Convert to float range
try:
self.train = ImageDatasetCreator._convert_image_dtype(dataset["train"])
self.test = ImageDatasetCreator._convert_image_dtype(dataset["test"])
except KeyError as exception:
raise ValueError(
f"Dataset {name} does not have train and test, write your own custom dataset handler."
) from exception
if cache:
self.train = self.train.cache() # speed things up considerably
self.test = self.test.cache()
self.batch: int = batch
def get_train(self):
return self.train.shuffle().batch(self.batch).repeat()
def get_test(self):
return self.test.batch(self.batch).repeat()
So now you can load more than mnist using simple command:
from datasets import ImageDatasetCreator
if __name__ == "__main__":
dataloader = ImageDatasetCreator("mnist", batch=64, cache = True)
train, test = dataloader.get_train(), dataloader.get_test()
And you could use any name other than mnist you want to load datasets from now on.
Please, stop making everything deep learning related one hand-off scripts, you are a programmer as well.
1.2 Model creation
Since tf2.0 there are two advised ways one can proceed depending on models complexity:
tensorflow.keras.models.Sequential - this way was shown by #Stewart_R, no need to reiterate his points. Used for the simplest models (you should use this one with your feedforward).
Inheriting tensorflow.keras.Model and writing custom model. This one should be used when you have some kind of logic inside your module or it's more complicated (things like ResNets, multipath networks etc.). All in all more readable and customizable.
Your Model class tried to resemble something like that but it went south again; backprop definitely is not part of the model itself, neither is loss or accuracy, separate them into another module or function, defo not a member!
That said, let's code the network using the second approach (you should place this code in model.py for brevity). Before that, I will code YourDense feedforward layer from scratch by inheriting from tf.keras.Layers (this one might go into layers.py module):
import tensorflow as tf
class YourDense(tf.keras.layers.Layer):
def __init__(self, units):
# It's Python 3, you don't have to specify super parents explicitly
super().__init__()
self.units = units
# Use build to create variables, as shape can be inferred from previous layers
# If you were to create layers in __init__, one would have to provide input_shape
# (same as it occurs in PyTorch for example)
def build(self, input_shape):
# You could use different initializers here as well
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer="random_normal",
trainable=True,
)
# You could define bias in __init__ as well as it's not input dependent
self.bias = self.add_weight(shape=(self.units,), initializer="random_normal")
# Oh, trainable=True is default
def call(self, inputs):
# Use overloaded operators instead of tf.add, better readability
return tf.matmul(inputs, self.kernel) + self.bias
Regarding your
How to add a Dropout and Batch Normalization layer in this custom
implementation? (i.e making it work for both train and test time)
I suppose you would like to create a custom implementation of those layers.
If not, you can just import from tensorflow.keras.layers import Dropout and use it anywhere you want as #Leevo pointed out.
Inverted dropout with different behaviour during train and test below:
class CustomDropout(layers.Layer):
def __init__(self, rate, **kwargs):
super().__init__(**kwargs)
self.rate = rate
def call(self, inputs, training=None):
if training:
# You could simply create binary mask and multiply here
return tf.nn.dropout(inputs, rate=self.rate)
# You would need to multiply by dropout rate if you were to do that
return inputs
Layers taken from here and modified to better fit showcasing purpose.
Now you can create your model finally (simple double feedforward):
import tensorflow as tf
from layers import YourDense
class Model(tf.keras.Model):
def __init__(self):
super().__init__()
# Use Sequential here for readability
self.network = tf.keras.Sequential(
[YourDense(100), tf.keras.layers.ReLU(), YourDense(10)]
)
def call(self, inputs):
# You can use non-parametric layers inside call as well
flattened = tf.keras.layers.Flatten()(inputs)
return self.network(flattened)
Ofc, you should use built-ins as much as possible in general implementations.
This structure is pretty extensible, so generalization to convolutional nets, resnets, senets, whatever should be done via this module. You can read more about it here.
I think it fulfills your 5th point:
I also want help in writing this code in a more generalized way so
I can easily implement other networks like ConvNets (i.e Conv, MaxPool
etc.) based on this code easily.
Last thing, you may have to use model.build(shape) in order to build your model's graph.
model.build((None, 28, 28, 1))
This would be for MNIST's 28x28x1 input shape, where None stands for batch.
1.3 Training
Once again, training could be done in two separate ways:
standard Keras model.fit(dataset) - useful in simple tasks like classification
tf.GradientTape - more complicated training schemes, most prominent example would be Generative Adversarial Networks, where two models optimize orthogonal goals playing minmax game
As pointed out by #Leevo once again, if you are to use the second way, you won't be able to simply use callbacks provided by Keras, hence I'd advise to stick with the first option whenever possible.
In theory you could call callback's functions manually like on_batch_begin() and others where needed, but it would be cumbersome and I'm not sure how would this work.
When it comes to the first option, you can use tf.data.Dataset objects directly with fit. Here is it presented inside another module (preferably train.py):
def train(
model: tf.keras.Model,
path: str,
train: tf.data.Dataset,
epochs: int,
steps_per_epoch: int,
validation: tf.data.Dataset,
steps_per_validation: int,
stopping_epochs: int,
optimizer=tf.optimizers.Adam(),
):
model.compile(
optimizer=optimizer,
# I used logits as output from the last layer, hence this
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.metrics.SparseCategoricalAccuracy()],
)
model.fit(
train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=validation,
validation_steps=steps_per_validation,
callbacks=[
# Tensorboard logging
tf.keras.callbacks.TensorBoard(
pathlib.Path("logs")
/ pathlib.Path(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")),
histogram_freq=1,
),
# Early stopping with best weights preserving
tf.keras.callbacks.EarlyStopping(
monitor="val_sparse_categorical_accuracy",
patience=stopping_epochs,
restore_best_weights=True,
),
],
)
model.save(path)
More complicated approach is very similar (almost copy and paste) to PyTorch training loops, so if you are familiar with those, they should not pose much of a problem.
You can find examples throughout tf2.0 docs, e.g. here or here.
2. Other things
2.1 Unanswered questions
Is there anything else in the code that I can optimize further in
this code? i.e (making use of tensorflow 2.x #tf.function decorator
etc.)
Above already transforms the Model into graphs, hence I don't think you would benefit from calling it in this case. And premature optimization is the root of all evil, remember to measure your code before doing this.
You would gain much more with proper caching of data (as described at the beginning of #1.1) and good pipeline rather than those.
Also I need a way to extract all my final weights for all layers
after training so I can plot them and check their distributions. To
check issues like gradient vanishing or exploding.
As pointed out by #Leevo above,
weights = model.get_weights()
Would get you the weights. You may transform them into np.array and plot using seaborn, matplotlib, analyze, check or whatever else you want.
2.2 Putting it altogether
All in all, your main.py (or entrypoint or something similar) would consist of this (more or less):
from dataset import ImageDatasetCreator
from model import Model
from train import train
# You could use argparse for things like batch, epochs etc.
if __name__ == "__main__":
dataloader = ImageDatasetCreator("mnist", batch=64, cache=True)
train, test = dataloader.get_train(), dataloader.get_test()
model = Model()
model.build((None, 28, 28, 1))
train(
model, train, path epochs, test, len(train) // batch, len(test) // batch, ...
) # provide necessary arguments appropriately
# Do whatever you want with those
weights = model.get_weights()
Oh, remember that above functions are not for copy pasting and should be treated more like a guideline. Hit me up if you have any questions.
3. Questions from comments
3.1 How to initialize custom and built-in layers
3.1.1 TLDR what you are about to read
Custom Poisson initalization function, but it takes three
arguments
tf.keras.initalization API needs two arguments (see last point in their docs), hence one is
specified via Python's lambda inside custom layer we have written before
Optional bias for the layer is added, which can be turned off with
boolean
Why is it so uselessly complicated? To show that in tf2.0 you can finally use Python's functionality, no more graph hassle, if instead of tf.cond etc.
3.1.2 From TLDR to implementation
Keras initializers can be found here and Tensorflow's flavor here.
Please note API inconsistencies (capital letters like classes, small letters with underscore like functions), especially in tf2.0, but that's beside the point.
You can use them by passing a string (as it's done in YourDense above) or during object creation.
To allow for custom initialization in your custom layers, you can simply add additional argument to the constructor (tf.keras.Model class is still Python class and it's __init__ should be used same as Python's).
Before that, I will show you how to create custom initialization:
# Poisson custom initialization because why not.
def my_dumb_init(shape, lam, dtype=None):
return tf.squeeze(tf.random.poisson(shape, lam, dtype=dtype))
Notice, it's signature takes three arguments, while it should take (shape, dtype) only. Still, one can "fix" this easily while creating his own layer, like the one below (extended YourLinear):
import typing
import tensorflow as tf
class YourDense(tf.keras.layers.Layer):
# It's still Python, use it as Python, that's the point of tf.2.0
#classmethod
def register_initialization(cls, initializer):
# Set defaults if init not provided by user
if initializer is None:
# let's make the signature proper for init in tf.keras
return lambda shape, dtype: my_dumb_init(shape, 1, dtype)
return initializer
def __init__(
self,
units: int,
bias: bool = True,
# can be string or callable, some typing info added as well...
kernel_initializer: typing.Union[str, typing.Callable] = None,
bias_initializer: typing.Union[str, typing.Callable] = None,
):
super().__init__()
self.units: int = units
self.kernel_initializer = YourDense.register_initialization(kernel_initializer)
if bias:
self.bias_initializer = YourDense.register_initialization(bias_initializer)
else:
self.bias_initializer = None
def build(self, input_shape):
# Simply pass your init here
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer=self.kernel_initializer,
trainable=True,
)
if self.bias_initializer is not None:
self.bias = self.add_weight(
shape=(self.units,), initializer=self.bias_initializer
)
else:
self.bias = None
def call(self, inputs):
weights = tf.matmul(inputs, self.kernel)
if self.bias is not None:
return weights + self.bias
I have added my_dumb_initialization as the default (if user does not provide one) and made the bias optional with bias argument. Note you can use if freely as long as it's not data dependent. If it is (or is dependent on tf.Tensor somehow), one has to use #tf.function decorator which changes Python's flow to it's tensorflow counterpart (e.g. if to tf.cond).
See here for more on autograph, it's very easy to follow.
If you want to incorporate above initializer changes into your model, you have to create appropriate object and that's it.
... # Previous of code Model here
self.network = tf.keras.Sequential(
[
YourDense(100, bias=False, kernel_initializer="lecun_uniform"),
tf.keras.layers.ReLU(),
YourDense(10, bias_initializer=tf.initializers.Ones()),
]
)
... # and the same afterwards
With built-in tf.keras.layers.Dense layers, one can do the same (arguments names differ, but idea holds).
3.2 Automatic Differentiation using tf.GradientTape
3.2.1 Intro
Point of tf.GradientTape is to allow users normal Python control flow and gradient calculation of variables with respect to another variable.
Example taken from here but broken into separate pieces:
def f(x, y):
output = 1.0
for i in range(y):
if i > 1 and i < 5:
output = tf.multiply(output, x)
return output
Regular python function with for and if flow control statements
def grad(x, y):
with tf.GradientTape() as t:
t.watch(x)
out = f(x, y)
return t.gradient(out, x)
Using gradient tape you can record all operations on Tensors (and their intermediate states as well) and "play" it backwards (perform automatic backward differentiation using chaing rule).
Every Tensor within tf.GradientTape() context manager is recorded automatically. If some Tensor is out of scope, use watch() method as one can see above.
Finally, gradient of output with respect to x (input is returned).
3.2.2 Connection with deep learning
What was described above is backpropagation algorithm. Gradients w.r.t (with respect to) outputs are calculated for each node in the network (or rather for every layer). Those gradients are then used by various optimizers to make corrections and so it repeats.
Let's continue and assume you have your tf.keras.Model, optimizer instance, tf.data.Dataset and loss function already set up.
One can define a Trainer class which will perform training for us. Please read comments in the code if in doubt:
class Trainer:
def __init__(self, model, optimizer, loss_function):
self.model = model
self.loss_function = loss_function
self.optimizer = optimizer
# You could pass custom metrics in constructor
# and adjust train_step and test_step accordingly
self.train_loss = tf.keras.metrics.Mean(name="train_loss")
self.test_loss = tf.keras.metrics.Mean(name="train_loss")
def train_step(self, x, y):
# Setup tape
with tf.GradientTape() as tape:
# Get current predictions of network
y_pred = self.model(x)
# Calculate loss generated by predictions
loss = self.loss_function(y, y_pred)
# Get gradients of loss w.r.t. EVERY trainable variable (iterable returned)
gradients = tape.gradient(loss, self.model.trainable_variables)
# Change trainable variable values according to gradient by applying optimizer policy
self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
# Record loss of current step
self.train_loss(loss)
def train(self, dataset):
# For N epochs iterate over dataset and perform train steps each time
for x, y in dataset:
self.train_step(x, y)
def test_step(self, x, y):
# Record test loss separately
self.test_loss(self.loss_function(y, self.model(x)))
def test(self, dataset):
# Iterate over whole dataset
for x, y in dataset:
self.test_step(x, y)
def __str__(self):
# You need Python 3.7 with f-string support
# Just return metrics
return f"Loss: {self.train_loss.result()}, Test Loss: {self.test_loss.result()}"
Now, you could use this class in your code really simply like this:
EPOCHS = 5
# model, optimizer, loss defined beforehand
trainer = Trainer(model, optimizer, loss)
for _ in range(EPOCHS):
trainer.train(train_dataset) # Same for training and test datasets
trainer.test(test_dataset)
print(f"Epoch {epoch}: {trainer})")
Print would tell you training and test loss for each epoch. You can mix training and testing any way you want (e.g. 5 epochs for training and 1 testing), you could add different metrics etc.
See here if you want non-OOP oriented approach (IMO less readable, but to each it's own).

Also If there's something I could improve in the code do let me know
as well.
Embrace the high-level API for something like this. You can do it in just a few lines of code and it's much easier to debug, read and reason about:
(x_train, y_train), (x_test, y_test) = tfds.load('mnist', split=['train', 'test'],
batch_size=-1, as_supervised=True)
x_train = tf.cast(tf.reshape(x_train, shape=(x_train.shape[0], 784)), tf.float32)
x_test = tf.cast(tf.reshape(x_test, shape=(x_test.shape[0], 784)), tf.float32)
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(512, activation='sigmoid'),
tf.keras.layers.Dense(256, activation='sigmoid'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

I tried to write a custom implementation of basic neural network with
two hidden layers on MNIST dataset using tensorflow 2.0 beta but I'm
not sure what went wrong here but my training loss and accuracy seems
to stuck at 1.5 and around85's respectively.
Where is the training part? Training of TF 2.0 models either Keras' syntax or Eager execution with tf.GradientTape(). Can you paste the code with conv and dense layers, and how you trained it?
Other questions:
1) How to add a Dropout layer in this custom implementation? i.e
(making it work for both train and test time)
You can add a Dropout() layer with:
from tensorflow.keras.layers import Dropout
And then you insert it into a Sequential() model just with:
Dropout(dprob) # where dprob = dropout probability
2) How to add Batch Normalization in this code?
Same as before, with:
from tensorflow.keras.layers import BatchNormalization
The choise of where to put batchnorm in the model, well, that's up to you. There is no rule of thumb, I suggest you to make experiments. With ML it's always a trial and error process.
3) How can I use callbacks in this code? i.e (making use of
EarlyStopping and ModelCheckpoint callbacks)
If you are training using Keras' syntax, you can simply use that. Please check this very thorough tutorial on how to use it. It just takes few lines of code.
If you are running a model in Eager execution, you have to implement these techniques yourself, with your own code. It's more complex, but it also gives you more freedom in the implementation.
4) Is there anything else in the code that I can optimize further in
this code? i.e (making use of tensorflow 2.x #tf.function decorator
etc.)
It depends. If you are using Keras syntax, I don't think you need to add more to it. In case you are training the model in Eager execution, then I'd suggest you to use the #tf.function decorator on some function to speed up a bit.
You can see a practical TF 2.0 example on how to use the decorator in this Notebook.
Other than this, I suggest you to play with regularization techniques such as weights initializations, L1-L2 loss, etc.
5) Also I need a way to extract all my final weights for all layers
after training so I can plot them and check their distributions. To
check issues like gradient vanishing or exploding.
Once the model is trained, you can extract its weights with:
weights = model.get_weights()
or:
weights = model.trainable_weights
If you want to keep only trainable ones.
6) I also want help in writing this code in a more generalized way so
I can easily implement other networks like convolutional network (i.e
Conv, MaxPool etc.) based on this code easily.
You can pack all your code into a function, then . At the end of this Notebook I did something like this (it's for a feed-forward NN, which is much more simple, but that's a start and you can change the code according to your needs).
---
UPDATE:
Please check my TensorFlow 2.0 implementaion of a CNN classifier. This might be a useful hint: it is trained on the Fashion MNIST dataset, which makes it very similar to your task.

Tensorflow 2.0 doesn't compute the gradient

I want to visualize the patterns that a given feature map in a CNN has learned (in this example I'm using vgg16). To do so I create a random image, feed through the network up to the desired convolutional layer, choose the feature map and find the gradients with the respect to the input. The idea is to change the input in such a way that will maximize the activation of the desired feature map. Using tensorflow 2.0 I have a GradientTape that follows the function and then computes the gradient, however the gradient returns None, why is it unable to compute the gradient?
import tensorflow as tf
import matplotlib.pyplot as plt
import time
import numpy as np
from tensorflow.keras.applications import vgg16
class maxFeatureMap():
def __init__(self, model):
self.model = model
self.optimizer = tf.keras.optimizers.Adam()
def getNumLayers(self, layer_name):
for layer in self.model.layers:
if layer.name == layer_name:
weights = layer.get_weights()
num = weights[1].shape[0]
return ("There are {} feature maps in {}".format(num, layer_name))
def getGradient(self, layer, feature_map):
pic = vgg16.preprocess_input(np.random.uniform(size=(1,96,96,3))) ## Creates values between 0 and 1
pic = tf.convert_to_tensor(pic)
model = tf.keras.Model(inputs=self.model.inputs,
outputs=self.model.layers[layer].output)
with tf.GradientTape() as tape:
## predicts the output of the model and only chooses the feature_map indicated
predictions = model.predict(pic, steps=1)[0][:,:,feature_map]
loss = tf.reduce_mean(predictions)
print(loss)
gradients = tape.gradient(loss, pic[0])
print(gradients)
self.optimizer.apply_gradients(zip(gradients, pic))
model = vgg16.VGG16(weights='imagenet', include_top=False)
x = maxFeatureMap(model)
x.getGradient(1, 24)

This is a common pitfall with GradientTape; the tape only traces tensors that are set to be "watched" and by default tapes will watch only trainable variables (meaning tf.Variable objects created with trainable=True). To watch the pic tensor, you should add tape.watch(pic) as the very first line inside the tape context.
Also, I'm not sure if the indexing (pic[0]) will work, so you might want to remove that -- since pic has just one entry in the first dimension it shouldn't matter anyway.
Furthermore, you cannot use model.predict because this returns a numpy array, which basically "destroys" the computation graph chain so gradients won't be backpropagated. You should simply use the model as a callable, i.e. predictions = model(pic).

Did you define your own loss function? Did you convert tensor to numpy in your loss function?
As a freshman, I also met the same problem:
When using tape.gradient(loss, variables), it turns out None because I convert tensor to numpy array in my own loss function. It seems to be a stupid but common mistake for freshman.

FYI: When GradientTape is not working, there is a possibility of TensorFlow issue. Checking the TF github if the TF functions being used have known issues would be one of the problem determinations.
Gradients do not exist for variables after tf.concat(). #37726.

Deal with imbalanced dataset in text classification with Keras and Theano

For ~20,000 text datasets, the true and false samples are ~5,000 against ~1,5000. Two-channel textCNN built with Keras and Theano is used to do the classification. F1 score is the evaluation metric. The F1 score is not bad while the confusion matrix shows that the accuracy of the true samples is relatively low(~40%). But actually it is very important to predict the true samples accurately. Therefore, want to design a custom binary cross entropy loss function to increase the weight of mis-classified true samples and make the model focus more on predicting accurately on the true samples.
tried class_weight with sklearn in model.fit method and it did not work very well since the weight applied to all samples instead of the mis-classified ones.
tried and adjusted the method mentioned here: https://github.com/keras-team/keras/issues/2115, but the loss function was categorical cross entropy and it did not work well for the binary classification problem. Tried to modified the loss function to a binary one but encounter some issues concerning the input dimension.
The sample code of the cost sensitive loss function focusing on the mis-classified samples is:
def w_categorical_crossentropy(y_true, y_pred, weights):
nb_cl = len(weights)
final_mask = K.zeros_like(y_pred[:, 0])
y_pred_max = K.max(y_pred, axis=1)
y_pred_max = K.reshape(y_pred_max, (K.shape(y_pred)[0], 1))
y_pred_max_mat = K.equal(y_pred, y_pred_max)
for c_p, c_t in product(range(nb_cl), range(nb_cl)):
final_mask += (weights[c_t, c_p] * y_pred_max_mat[:, c_p] * y_true[:, c_t])
return K.categorical_crossentropy(y_pred, y_true) * final_mask
Actually, a custom loss function for binary classification implemented with Keras and Theano that focuses on the mis-classified samples is of great importance to the imbalanced dataset. Please help troubleshoot this. Thanks!

Well when I have to deal with imbalanced datasets in keras, what I do is to first compute the weights for each class and pass them to the model instance during training. This will look something like this:
from sklearn.utils import compute_class_weight
w = compute_class_weight('balanced', np.unique(targets), targets)
# here I am adding only two categories with their corresponding weights
# you can spin a loop or continue by hand until you include all of your categories
weights = {
np.unique(targets)[0] : w[0], # class 0 with weight 0
np.unique(targets)[1] : w[1] # class 1 with weight 1
}
# then during training you do like this
model.fit(x=features, y=targets, {..}, class_weight=weights)
I believe this will solve your problem.

How do masked values affect the metrics in Keras?

If I look into keras metric I see that the values of y_true and y_predict are "just" compared at the end of each epoch for categorical_accuracy:
def categorical_accuracy(y_true, y_pred):
return K.cast(K.equal(K.argmax(y_true, axis=-1),
K.argmax(y_pred, axis=-1)),
K.floatx())
How are masked values handled? If I understood correctly, masking prohibits the masked values to influence the training, but it still produces predictions for the masked values. Thereby, it does, in my opinion, influence the metric.
More explanation on how it influences the metric:
In the padding/masking process, I set the padded/masked values in y_true to an unused class e.g. class 0.
If now argmax() is looking for a max value in the one-hot encoded y_true, it will just return 0 as the total (masked) row is the same.
I do not have a class 0, as it is my masking value/class, and thereby the y_pred and y_true certainly have different values creating a reduced accuracy.
Is this somehow already thought of in the Keras metric and I oversaw it?
Otherwise, I would have to create a custom metric or callback creating a similar metric to categorical_accuracy with the addition that all masked values are eliminated in y_pred and y_true before comparison.

Maybe the best answer would be this from Keras.metrics :
A metric function is similar to a loss function, except that the results from evaluating a metric are not used when training the model.
The training is only influenced by the loss function where masking is implemented.
Nevertheless, your displayed results are not on par with the actual results and can lead to misleading conclusions.
As the metric is not used in the training process, a callback function can solve this.
something like this (based on Andrew Ng). I Search for 0 here as for my masked target all one-hot-encoded targets are 0 (No class activated).
import numpy as np
from keras.callbacks import Callback
from sklearn.metrics import accuracy_score
class categorical_accuracy_no_mask(Callback):
def on_train_begin(self, logs={}):
self.val_acc = []
def on_epoch_end(self, epoch, logs={}):
val_predict = (np.asarray(self.model.predict(self.model.validation_data[0]))).round()
val_targ = self.model.validation_data[1]
indx = np.where(~val_targ.any(axis=2))[0] #find where all targets are zero. That are the masked once as we masked the target with 0 and the data with 666
y_true_nomask = numpy.delete(val_targe, indx, axis=0)
y_pred_nomask = numpy.delete(val_predict, indx, axis=0)
_val_accuracy = accuracy_score(y_true_nomask, y_pred_nomask)
self.val_acc.append(_val_accuracy)
print “ — val_accuracy : %f ” %( _val_accuracy )
return
Of course, now you could add also precision-recall etc.

How is embedding matrix being trained in this code snippet?

I'm following the code of a coursera assignment which implements a NER tagger using a bidirectional LSTM.
But I'm not able to understand how the embedding matrix is being updated. In the following code, build_layers has a variable embedding_matrix_variable which acts an input the the LSTM. However it's not getting updated anywhere.
Can you help me understand how embeddings are being trained?
def build_layers(self, vocabulary_size, embedding_dim, n_hidden_rnn, n_tags):
initial_embedding_matrix = np.random.randn(vocabulary_size, embedding_dim) / np.sqrt(embedding_dim)
embedding_matrix_variable = tf.Variable(initial_embedding_matrix, name='embedding_matrix', dtype=tf.float32)
forward_cell = tf.nn.rnn_cell.DropoutWrapper(
tf.nn.rnn_cell.BasicLSTMCell(num_units=n_hidden_rnn, forget_bias=3.0),
input_keep_prob=self.dropout_ph,
output_keep_prob=self.dropout_ph,
state_keep_prob=self.dropout_ph
)
backward_cell = tf.nn.rnn_cell.DropoutWrapper(
tf.nn.rnn_cell.BasicLSTMCell(num_units=n_hidden_rnn, forget_bias=3.0),
input_keep_prob=self.dropout_ph,
output_keep_prob=self.dropout_ph,
state_keep_prob=self.dropout_ph
)
embeddings = tf.nn.embedding_lookup(embedding_matrix_variable, self.input_batch)
(rnn_output_fw, rnn_output_bw), _ = tf.nn.bidirectional_dynamic_rnn(
cell_fw=forward_cell, cell_bw=backward_cell,
dtype=tf.float32,
inputs=embeddings,
sequence_length=self.lengths
)
rnn_output = tf.concat([rnn_output_fw, rnn_output_bw], axis=2)
self.logits = tf.layers.dense(rnn_output, n_tags, activation=None)
def compute_loss(self, n_tags, PAD_index):
"""Computes masked cross-entopy loss with logits."""
ground_truth_tags_one_hot = tf.one_hot(self.ground_truth_tags, n_tags)
loss_tensor = tf.nn.softmax_cross_entropy_with_logits(labels=ground_truth_tags_one_hot, logits=self.logits)
mask = tf.cast(tf.not_equal(self.input_batch, PAD_index), tf.float32)
self.loss = tf.reduce_mean(tf.reduce_sum(tf.multiply(loss_tensor, mask), axis=-1) / tf.reduce_sum(mask, axis=-1))

In TensorFlow, variables are not usually updated directly (i.e. by manually setting them to a certain value), but rather they are trained using an optimization algorithm and automatic differentiation.
When you define a tf.Variable, you are adding a node (that maintains a state) to the computational graph. At training time, if the loss node depends on the state of the variable that you defined, TensorFlow will compute the gradient of the loss function with respect to that variable by automatically following the chain rule through the computational graph. Then, the optimization algorithm will make use of the computed gradients to update the values of the trainable variables that took part in the computation of the loss.
Concretely, the code that you provide builds a TensorFlow graph in which the loss self.loss depends on the weights in embedding_matrix_variable (i.e. there is a path between these nodes in the graph), so TensorFlow will compute the gradient with respect to this variable, and the optimizer will update its values when minimizing the loss. It might be useful to inspect the TensorFlow graph using TensorBoard.