"Keras is an open source neural network library written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
Use Keras if you need a deep learning libraty that:
- Allows for easy and fast prototyping
- Supports both convolutional networks and recurrent networks, as well as combinations of the two
- Runs seamlessly on CPU and GPU
Keras is compatible with Python 2.7-3.5"[1].
Since Semptember 2016, Keras is the second-fastest growing Deep Learning framework after Google's Tensorflow, and the third largest after Tensorflow and Caffe[2].
"Deep Learning is the application to learning tasks of artificial neural networks(ANNs) that contain more than one hidden layer. Deep learning is part of Machine Learning methods based on learning data representations. Learning can be supervised, parially supervised or unsupervised[3]."
You will learn:
- What is Keras library and how to use it
- What is Deep Learning
- How to use ready datasets
- What is Convolutional Neural Networks(CNN)
- How to build step by step Convolutional Neural Networks(CNN)
- What are differences in model results
- What is supervised and unsupervised learning
- Basics of Machine Learning
- Introduction to Artificial Intelligence(AI)
- vgg.py - simple example of VGG16 neural net
- README.md - description of this project
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### 3 32 32
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 3 34 34
Conv2D \|/ ------------------- 1792 0.0%
relu ##### 64 32 32
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 64 34 34
Conv2D \|/ ------------------- 36928 0.1%
relu ##### 64 32 32
MaxPooling2D Y max ------------------- 0 0.0%
##### 64 16 16
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 64 18 18
Conv2D \|/ ------------------- 73856 0.2%
relu ##### 128 16 16
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 128 18 18
Conv2D \|/ ------------------- 147584 0.4%
relu ##### 128 16 16
MaxPooling2D Y max ------------------- 0 0.0%
##### 128 8 8
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 128 10 10
Conv2D \|/ ------------------- 295168 0.9%
relu ##### 256 8 8
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 256 10 10
Conv2D \|/ ------------------- 590080 1.8%
relu ##### 256 8 8
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 256 10 10
Conv2D \|/ ------------------- 590080 1.8%
relu ##### 256 8 8
MaxPooling2D Y max ------------------- 0 0.0%
##### 256 4 4
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 256 6 6
Conv2D \|/ ------------------- 1180160 3.5%
relu ##### 512 4 4
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 512 6 6
Conv2D \|/ ------------------- 2359808 7.0%
relu ##### 512 4 4
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 512 6 6
Conv2D \|/ ------------------- 2359808 7.0%
relu ##### 512 4 4
MaxPooling2D Y max ------------------- 0 0.0%
##### 512 2 2
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 512 4 4
Conv2D \|/ ------------------- 2359808 7.0%
relu ##### 512 2 2
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 512 4 4
Conv2D \|/ ------------------- 2359808 7.0%
relu ##### 512 2 2
ZeroPadding2D \|||/ ------------------- 0 0.0%
##### 512 4 4
Conv2D \|/ ------------------- 2359808 7.0%
relu ##### 512 2 2
MaxPooling2D Y max ------------------- 0 0.0%
##### 512 1 1
Flatten ||||| ------------------- 0 0.0%
##### 512
Dense XXXXX ------------------- 2101248 6.2%
relu ##### 4096
Dropout | || ------------------- 0 0.0%
##### 4096
Dense XXXXX ------------------- 16781312 49.9%
relu ##### 4096
Dropout | || ------------------- 0 0.0%
##### 4096
Dense XXXXX ------------------- 40970 0.1%
softmax ##### 10
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=x_train.shape[1:]))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-6, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
cnn_n = base_model()
cnn_n.summary()
cnn = cnn_n.fit(x_train,y_train, batch_size=batch_size, epochs=epochs,validation_data=(x_test,y_test),shuffle=True)
All results are for 50k iteration, learning rate=0.01. Neural networks have benn trained at 16 cores and 16GB RAM on plon.io.
- epochs = 10 accuracy=10.0%
Confusion matrix result:
[[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]
[ 0 0 0 0 0 0 0 0 0 1000]]
Confusion matrix vizualizing
Time of learning process: xxx
- epochs = 20 accuracy=%
Confusion matrix result:
Confusion matrix vizualizing
Time of learning process: xxx
- epochs = 50 accuracy=%
Confusion matrix result:
Confusion matrix vizualizing
Time of learning process: xxx
- epochs = 100 accuracy=%
Confusion matrix result:
Confusion matrix vizualizing
Time of learning process: xxx
.
You can also check here my other Keras Cifar-10 classification using 4,6-layer neural nets.
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