UnilNet_K.py

UnilNet_K.py

# -*- coding: utf-8 -*-
"""
Created on Tue Dec 19 12:27:25 2017

@author: mlaib
"""

import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from keras.layers import Conv2D, Dense, Input, GlobalMaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.optimizers import Adamax
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
import scipy.io as sc

mat4 = sc.loadmat('D:/Data/sat-4-full.mat')
mat4.keys()
train_X = np.moveaxis(mat4['train_x'], 3, 0)
train_Y = np.swapaxes(mat4['train_y'], 1, 0)
test_X = np.moveaxis(mat4['test_x'], 3, 0)
test_Y = np.swapaxes(mat4['test_y'], 1, 0)
print("Data loaded")
X1_train, X1_valid, yy_train, yy_valid = train_test_split(train_X, train_Y, random_state=1, train_size=0.8)

# plot some images
def show_it(ID, Data=train_X ,CL=train_Y):
    r = Data[ID,:,:,0]
    g = Data[ID,:,:,1]
    b = Data[ID,:,:,2]
    rgbArray = np.zeros((28,28,3), 'uint8')
    Rband = r.astype(np.float)
    Gband = g.astype(np.float)
    Bband = b.astype(np.float)
    rgbArray[..., 0] = Rband
    rgbArray[..., 1] = Gband
    rgbArray[..., 2] = Bband
    plt.imshow(rgbArray) 
    plt.title('Class' + str(CL[ID,:]))
    ax = plt.gca(); ax.ticklabel_format(useOffset=False, style='plain') 
    plt.setp(ax.get_xticklabels(),rotation=90) 
    
show_it(ID=10130, Data=train_X ,CL=test_Y)
show_it(ID=11, Data=test_X ,CL=train_Y)

def get_model():
    bn_model = 0
    p_activation = "elu"
    input_1 = Input(shape=(28, 28, 4), name="X_1")
    img_1 = Conv2D(32, kernel_size = (3,3), activation=p_activation) ((BatchNormalization(momentum=bn_model))(input_1))
    #img_1 = Conv2D(64, kernel_size = (3,3), activation=p_activation) (img_1) 
    img_1 = GlobalMaxPooling2D() (img_1)
    dense_ayer = Dense(40, activation=p_activation)(img_1) 
    output = Dense(4, activation="softmax")(dense_ayer)       
    model = Model(input_1,  output)
    optimizer = Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
    model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
    return model
model = get_model()

earlyStopping = EarlyStopping(monitor='val_loss', patience=20, verbose=0, mode='min')
mcp_save = ModelCheckpoint('wsat4.hdf5', save_best_only=True, monitor='val_loss', mode='min')
reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, epsilon=1e-4, mode='min')

model.summary()
history = model.fit(X1_train, yy_train, epochs=30
          , validation_data=(X1_valid, yy_valid)
         , batch_size=900
         , callbacks=[earlyStopping, mcp_save, reduce_lr_loss])

print(history.history.keys())

fig = plt.figure()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')

fig = plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower left')

model.load_weights(filepath = 'wsat4.hdf5')

print("Test:")
print(model.evaluate(test_X, test_Y, verbose=1, batch_size=400))
print("The end")