CNN-based-solar-wind-prediction/CNN training and testing at master · Hemapriya-iit/CNN-based-solar-wind-prediction · GitHub

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train_x=np.load('/home/hemapriya/hema_data/sw_speed_2013_18/train_images.npy')
data_x = train_x.reshape(train_x.shape[0], 512,512,1)
print(data_x.shape)
test_2018=np.load('/home/hemapriya/hema_data/img_2018_new.npy')
test_x = test_2018.reshape(test_2018.shape[0], 512,512,1)
test_x_check=test_x[0:4331]
print(test_x_check.shape)
from sklearn.model_selection import train_test_split
train_x,val_x,train_y,val_y=train_test_split(data_x,trainy_speed,test_size=0.2,random_state = 0)
print(train_y.shape)
print(data_speed_2018.shape)
print(val_y.shape)
print(train_x.shape)
print(test_x.shape)
print(val_x.shape)


##CNN


import tensorflow as tf
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

config = ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.compat.v1.Session(config=config)

from keras.models import Model
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import convolutional
from keras.layers import pooling
from keras.layers import core
from keras import optimizers
from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.regularizers import l2
from keras.models import load_model

#model_3=Sequential()
model_8 = Sequential()
model_8.add(convolutional.Convolution2D(32, (9,9),strides=(3, 3), input_shape=(512,512,1)))
model_8.add(Activation('relu'))
model_8.add(pooling.MaxPooling2D(pool_size=(2, 2),strides=(2, 2)))
model_8.add(convolutional.Convolution2D(64, (2,2),strides=(2, 2)))
model_8.add(Activation('relu'))
model_8.add(pooling.MaxPooling2D(pool_size=(3, 3),strides=(2, 2)))
model_8.add(convolutional.Convolution2D(128, (2,2),strides=(2, 2)))
model_8.add(BatchNormalization())
model_8.add(Activation('relu'))
model_8.add(pooling.MaxPooling2D(pool_size=(3, 3),strides=(1, 1)))
model_8.add(Flatten())
model_8.add(Dense(4096,activation='relu'))
model_8.add(core.Dropout(.3))
model_8.add(Dense(1,activation='linear'))
model_8.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mse',metrics=['mse'])
print(model_8.summary())


##training

early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
history=model_8.fit(train_x,train_y,batch_size=128,epochs=20,validation_data=(val_x,val_y),callbacks=[early_stop])


##testing

model_8.save_weights('/home/hemapriya/hema_data/models/train_weights_183l.h5')
from keras.models import load_model
model_8.load_weights('/home/hemapriya/hema_data/models/train_weights_183l.h5')


y_test1=model_8.predict(test_x_check)

plt.figure(figsize=(25,7))
plt.ylim(200,800)
plt.plot((y_test1),label='predicted',linewidth=2.0)
plt.plot(data_speed_2018,label='actual',linewidth=3.0)
plt.title('CNN prediction for the year 2018',fontsize=30)
plt.xlabel('days',fontsize=25,c='white')
plt.ylabel('windspeed(Km/s)',fontsize=25,c='white')
plt.legend(fontsize=20)
plt.tick_params(labelsize=20)


plt.grid()