mlcourse.ai Open Machine learning course¶

Author:Natalia Domozhirova, slack: @ndomozhirova

KERAS : easy way to construct the Neural Networks¶

Introduction¶

Keras is a high-level neural networks API, written in Python.

Major Keras features:

• its capable of running on top of TensorFlow, CNTK, or Theano.
• Keras allows for easy and fast prototyping and supports both Perceptrons, Convolutional networks and Recurrent networks (including LSTM), as well as their combinations.
• Keras is compatible with: Python 2.7-3.6.

To make the process more interesting let's consider the classification example from the real life.

Example description¶

Let's take the task from one Hakaton, organized by some polypropylene producer this year. So, let’s consider the production of the polypropylene granules by the extruder. Extruder is a kind of “meat grinder” which has the knives at the end of the process which are cutting the output product onto the granules.
The problem is that sometimes the production mass has an irregular consistency and sticks to the knives. When there is a lot of stuck mass - knives can no longer function. In this case it is necessary to stop production process, which is very expensive. If we would catch the very beginning of such sticking process - there is a method to very quickly and painless clean the knives and continue production without stopping. So, the task is to send the stop signal to operator a bit in advance (let’s say not later then 15 minutes before such event) – so that he would have a time for necessary manipulations.

Now we have already preprocessed normalized dataset with the vectors of the system sensors' values (5,160 features) and 0/1 targets. It is already devided into the train and test. Let's download and prepare to work our datasets. In the datasets there are targets in zero column and the timestamps -in the 1st column.So, let's extract our train and test matrix as well as our targets. Also we'll transform the targets to categorical -so to have as a result our targets as 2-dimentional vectors, i.e. the vectors of probabilities of 0 and 1.

In [ ]:
import numpy as np
import pandas as pd
from keras.utils import np_utils

Y_train = np.array(df_train[0].values.astype(int))
Y_test = np.array(df_test[0].values.astype(int))
X_train = np.array(df_train.iloc[:, 2:].values.astype(float))
X_test = np.array(df_test.iloc[:, 2:].values.astype(float))

Y_train = Y_train.astype(np.int)
Y_test = Y_test.astype(np.int)

Y_train = np_utils.to_categorical(Y_train, 2)
Y_test = np_utils.to_categorical(Y_test, 2)

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)


The Neural Network construction¶

Let's consider how the simple Newral Network(NN), Multilayer Perceptron (MLP), with 3 hidden layers (as a baseline), constructed by Keras, could help us to solve this problem.

As we have hidden layers - this would be a Deep Neural Network. Also, we can see, that we need to have 5160 neurons in the input layer, as this is the size of our vector X and 2 neurons in the last layer - as this is the size of our target (vs. the picture below, where there are 4 neurons on the output layer). You can read, for example, here or here some more information about MLP structure.

The core data structure of Keras is a model - a way to organize layers. The simplest type of model is the Sequential model, a linear stack of layers, which is appropriate for MLP construction (for more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers).

In [ ]:
import keras
from keras import Sequential

model1 = Sequential()


After the type of model is defined, we need to consistently add layers as Dense. Stacking layers is as easy as .add().

While adding the layer we need to define the number of neurons and Activation functions which we can tune afterwards. For the fist layer we also need to add the dimention of X vectors (input_dim). In our case this is 5,160. The last layer consists on 2 neurons exactly as our target vestors Y_train and Y_test do.

The number of layers can also be tuned.

In [ ]:
from keras.layers import Activation, Dense



Once our model looks good, we need to configure its learning process with .compile().

Here we should also describe the loss function and metrics we want to use as well as optimizer (the type of the Gradient descent to be used) which seem appropriate in each particular case.

In [ ]:
model1.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])


Now we can iterate on our training data in batches with the batch_size we want, where X_train and y_train are Numpy arrays just like in the Scikit-Learn API. Also we can define the number of epochs (i.e. the max number of the full cycles of model's training). Verbose=1 just lets us see the summary of the current stage of calcualtions.

We can also printing our model parameters using model.summary(). It is also can be useful to see the shapes of X_train, y_train,X_test,y_test

Also, we can save the best model version during the trainig process via the callback_save option.

And there is a callback_early stop option to stop the training process when we don't have significant improvement(defined by the min_delta) during the certain number of epochs (patience).

Now our first model is ready:

In [ ]:
from keras.callbacks import EarlyStopping, ModelCheckpoint

model1.summary()

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

callback_save = ModelCheckpoint(
"best_model1.model1", monitor="val_acc", verbose=1, save_best_only=True, mode="auto"
)
callback_earlystop = EarlyStopping(
monitor="val_loss", min_delta=0, patience=10, verbose=1, mode="auto"
)

model1.fit(
X_train,
Y_train,
batch_size=20,
epochs=10000,
verbose=1,
validation_data=(X_test, Y_test),
callbacks=[callback_save, callback_earlystop],
)


So, we got a baseline with Accuracy= 0.79. It looks cool, as we even didn't tune anything yet!

Let's try to improve this result. For example, we can introduce Dropout - this is a kind of regularization for the Neral Networks. The level of drop out (in the brackets, along with a seed) is a probability of the exclusion from the certain layer the random choice neuron during the current calculations. So, drop outs help to prevent the NN overfitting.
Let's create the new model:

In [ ]:
from keras.layers import Dropout

model2 = Sequential()

model2.summary()

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

callback_save = ModelCheckpoint(
"best_model2.model2", monitor="val_acc", verbose=1, save_best_only=True, mode="auto"
)
callback_earlystop = EarlyStopping(
monitor="val_loss", min_delta=0, patience=10, verbose=1, mode="auto"
)

model2.fit(
X_train,
Y_train,
batch_size=20,
epochs=10000,
verbose=1,
validation_data=(X_test, Y_test),
callbacks=[callback_save, callback_earlystop],
)


Thus, adding the drop-outs we've increased Accuracy on the test up to 0.86830

We can also tune all gyper-parameters like the number of layers, the levels of drop-outs, activation functions, optimizer, the number of neurons etc. For this purposes we can use, for example, another very friendly and easy-to-apply - Hyperas library. The description with examples you can find here. As a result of such tuning we've got the following model configuration:

In [ ]:
model3 = Sequential()

model3.compile(
loss="categorical_crossentropy", optimizer="rmsprop", metrics=["accuracy"]
)

model3.summary()

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

callback_save = ModelCheckpoint(
"best_model3.model3", monitor="val_acc", verbose=1, save_best_only=True, mode="auto"
)
callback_earlystop = EarlyStopping(
monitor="val_loss", min_delta=0, patience=10, verbose=1, mode="auto"
)

model3.fit(
X_train,
Y_train,
batch_size=60,
epochs=10000,
verbose=1,
validation_data=(X_test, Y_test),
callbacks=[callback_save, callback_earlystop],
)


Now, with tunned parameters, we've managed to imporove Accuracy up to 0.88073

With Keras it is also possible to use L1/L2 weight regularizations which allow to apply penalties on layer parameters or layer activity during optimization. These penalties are incorporated in the loss function that the network optimizers.Let's add some regularization on to the 1st layer.

In [ ]:
from keras import regularizers

model4 = Sequential()
Dense(
64,
input_dim=5160,
kernel_regularizer=regularizers.l2(0.0015),
activity_regularizer=regularizers.l1(0.0015),
)
)

model4.compile(
loss="categorical_crossentropy", optimizer="rmsprop", metrics=["accuracy"]
)

model4.summary()

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

callback_save = ModelCheckpoint(
"best_model4.model4", monitor="val_acc", verbose=1, save_best_only=True, mode="auto"
)
callback_earlystop = EarlyStopping(
monitor="val_loss", min_delta=0, patience=10, verbose=1, mode="auto"
)

model4.fit(
X_train,
Y_train,
batch_size=60,
epochs=10000,
verbose=1,
validation_data=(X_test, Y_test),
callbacks=[callback_save, callback_earlystop],
)


So, we can see, that adding regualrization with the current coeffitients to the firs layer we've got just Accuracy of 0.84421 which didn't improve the result. This means, that, as usual, they should be carefully tuned :)

When we want to use the best trained model we got, we can just download previously (automatically) saved the best one (via load_model) and apply to the data needed. Let's see what we'll get on the test set:

In [ ]:
from keras.models import load_model

result = model.predict_on_batch(X_test)
result[:5]


You may also be interested to get a list of all weight tensors of the model, as Numpy arrays via get_weights:

In [ ]:
weights = model.get_weights()
weights[:1]


Besides, you would propbably like to get the model config to re-use it in the future. This can be done via get_config:

In [ ]:
config = model.get_config()
config


So, the model can be reinstantiated from its config via from_config:

In [ ]:
model3 = Sequential.from_config(config)


For more model tuning options proposed by Keras pls see here

What about the other types of the Neural Networks?¶

Yes, you can use the similar approach re the layers' construction principles for LSTM, CNN and some other types of the Deep Neural Networks. For more details pls see here.