PV226 ML: Neural Networks
Content of this session
Introduction to NN
Keras
Different types of network and usages
Artificial Neuron
Activation Functions
ReLU
Sigmoid
Sigmoid Activation Function
-
Used for Binary Classification in the Logistic Regression model
-
The probabilities sum does not need to be 1
Softmax Activation Function
- Used for Multi-classification in the Logistics Regression model
- The probabilities sum will be 1
Tanh
Usage of tanh
- usually used in convolution layers
- or in LSTMs
Deep Neural Network
Weight initialization
- random
- ones
- zeroes
- ...
Application
- classification
- predictions
on
- image recognition
- language modeling
- time series
Neuron Types
From Neural Network Zoo
Most Basic Networks
Deep Network
Now those useful networks
Keras
the high-level API of TensorFlow 2
Installation
pip install tensorflowUsage
model = keras.Sequential(
[
keras.Input(shape=input_shape),
layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(num_classes, activation="softmax"),
]
)
model.summary()
batch_size = 128
epochs = 15
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(
x_train, y_train, batch_size=batch_size,
epochs=epochs, validation_split=0.1
)Evaluation
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])Any questions?
PV226: Neural Networks
By Lukáš Grolig
