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udacity self Layers Convolutional Neural Networks in Keras Pooling in Keras Dropout in Keras Compile, fit and evaluate the model

admin 11月 14, 2020 0

Keras make the coding for building a neural networks simpler.

The Sequential model in Keras is a wrapper for neural network model. It provides common functions like fit(), evaluate() and compile().

Layers

Use the model’s add() to add a layer:

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Flatten

# Create the Sequential model
model = Sequential()

#1st Layer - Add a flatten layer
model.add(Flatten(input_shape=(32, 32, 3)))

#2nd Layer - Add a fully connected layer
model.add(Dense(100))

#3rd Layer - Add a ReLU activation layer
model.add(Activation('relu'))

#4th Layer - Add a fully connected layer
model.add(Dense(60))

#5th Layer - Add a ReLU activation layer
model.add(Activation('relu'))

Keras will automatically infer the shape of each layer, which means you only need to set the input dimensions for the first layer.

Convolutional Neural Networks in Keras

from keras.models import Sequential
from keras.layers.convolutional import Conv2D

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3)))

Pooling in Keras

from keras.layers.pooling import MaxPooling2D

model.add(MaxPooling2D((2, 2)))

Dropout in Keras

from keras.layers.core import Dense, Activation, Flatten, Dropout

model.add(Dropout(0.5))

Compile, fit and evaluate the model

model.compile('adam', 'categorical_crossentropy', ['accuracy'])
history = model.fit(X, y, epochs=10, validation_split=0.2)
metrics = model.evaluate(X_test, y_test)