Keras: Deep Learning Library for Python

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Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano. We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation. Use Keras if you need a deep learning library that: -Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). -Supports both convolutional networks and recurrent networks, as well as combinations of the two. -Runs seamlessly on CPU and GPU.

Engenharia

What is keras?
● Keras is a high-level neural networks API, written in Python.
● Built on top of either Theano or TensorFlow.
● Most powerful & easy to use for developing and evaluating deep learning
models.

Why use Keras?
● Allows for easy and fast prototyping (through user friendliness, modularity,
and extensibility).
● Supports both convolutional networks and recurrent networks, as well as
combinations of the two.
● Runs seamlessly on CPU and GPU.

Creating a keras model
● Architecture Definition:-no of layers,no of nodes in layers,activation function
to be used.
● Compile:-defines the loss function and some details about how optimization
works.
● Fit:-cycle of backpropagation and optimization of model weights with your
data.
● Predict:-to predict the model prepared.

Keras code for creating model
The sequential model used is a linear stack of layers.
### Model begins ###
model = Sequential()
model.add(Convolution2D(16, 5, 5, activation='relu', input_shape=(img_width, img_height, 3)))
model.add(MaxPooling2D(2, 2))
model.add(Convolution2D(32, 5, 5, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(10, activation='softmax'))
### Model Ends ###

Compile model
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
● The loss function to use to evaluate a set of weights.
● The optimizer used to search through different weights for the network and any optional metrics we
would like to collect and report during training.
● For classification problem you will want to set this to metrics=[‘accuracy’]

Fit the model
# fit model
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
● Execution of model for some data.
● Train data and iterate data in batches.

Evaluate Model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
● It give us an idea of how well we have modeled the dataset.

Predict
classes=model.predict(x_test,batch_size=128)
● It generates prediction on new data

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Motoring and generation Armature circuit equation for motoring and generation, Types of field excitations - separately excited, shunt and series. Open circuit characteristic of separately excited DC generator, back EMF with armature reaction, voltage build-up in a shunt generator, critical field resistance and critical speed. V-I characteristics and torque-speed characteristics of separately excited shunt and series motors. Speed control through armature voltage. Losses, load testing and back-to-back testing of DC machines

DC MACHINE-Motoring and generation, Armature circuit equation

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This presentation takes a deep dive into the methodologies of HAZID and HAZOP, two cornerstone risk assessment techniques in the oil and gas industry. Over 12 slides, we compare the structured approaches of both HAZID and HAZOP, detail their individual steps, and discuss their benefits and drawbacks. The aim is to provide professionals with concise insight to make informed decisions about which method suits their project's needs. By the end, viewers will have understood each method's strategic value in the pursuit of workplace safety and efficiency.

Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...

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Keras: Deep Learning Library for Python

1. Keras A deep learning library

2. What is keras? ● Keras is a high-level neural networks API, written in Python. ● Built on top of either Theano or TensorFlow. ● Most powerful & easy to use for developing and evaluating deep learning models.

3. Why use Keras? ● Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). ● Supports both convolutional networks and recurrent networks, as well as combinations of the two. ● Runs seamlessly on CPU and GPU.

4. Creating a keras model ● Architecture Definition:-no of layers,no of nodes in layers,activation function to be used. ● Compile:-defines the loss function and some details about how optimization works. ● Fit:-cycle of backpropagation and optimization of model weights with your data. ● Predict:-to predict the model prepared.

5. Keras code for creating model The sequential model used is a linear stack of layers. ### Model begins ### model = Sequential() model.add(Convolution2D(16, 5, 5, activation='relu', input_shape=(img_width, img_height, 3))) model.add(MaxPooling2D(2, 2)) model.add(Convolution2D(32, 5, 5, activation='relu')) model.add(MaxPooling2D(2, 2)) model.add(Flatten()) model.add(Dense(1000, activation='relu')) model.add(Dense(10, activation='softmax')) ### Model Ends ###

6. Compile model # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) ● The loss function to use to evaluate a set of weights. ● The optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training. ● For classification problem you will want to set this to metrics=[‘accuracy’]

7. Fit the model # fit model model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) ● Execution of model for some data. ● Train data and iterate data in batches.

8. Evaluate Model score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) ● It give us an idea of how well we have modeled the dataset.

9. Predict classes=model.predict(x_test,batch_size=128) ● It generates prediction on new data

10. Thank you

Keras: Deep Learning Library for Python

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