convlstm2.py

from sklearn.model_selection import train_test_split
import numpy as np
from keras.utils.np_utils import to_categorical
from keras.models import Sequential,Model
from keras.layers import Dense, Dropout, Activation, Flatten,Input,TimeDistributed,BatchNormalization
from keras.layers import Convolution2D, ConvLSTM2D, MaxPooling2D, UpSampling2D,GlobalAveragePooling2D,AveragePooling3D,Reshape

np.random.seed(0)
data = np.random.randint(low=1,high=9,size=(99, 7, 11, 11, 3))
#(samples, timeshifts, heigt, width, nchannels)
print (data.shape)
#(99, 7, 11, 11, 3)

labels = np.random.randint(low=0,high=3,size=(99)) #(samples)
print (labels.shape)
#(99,)

classes = np.unique(labels)
print (classes)
#(0,1,2)


X_train, X_test, Y_train, Y_test = train_test_split(data,labels,test_size=0.30,random_state=0)

print ('X_train', X_train.shape)
print ('X_test', X_test.shape)
print ('Y_train', Y_train.shape)
print ('Y_test', Y_test.shape)


##X_train (69, 7, 11, 11, 3)
##X_test (30, 7, 11, 11, 3)
##Y_train (69,)
##Y_test (30,)


Y_train_ = to_categorical(Y_train) 
print ('Y_train_categorical', Y_train_.shape)

##Y_train_categorical (69, 2)

input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3], X_train.shape[4])
print ('input_shape', input_shape)

##input_shape (7, 11, 11, 3)
'''
model = Sequential()
model.add(ConvLSTM2D(16, kernel_size=(3,3), activation='sigmoid',padding='same',input_shape=input_shape,
                     return_sequences=True))
model.add(ConvLSTM2D(16, kernel_size=(3,3), activation='sigmoid',padding='same'))
model.add(Dense(len(classes), activation='softmax'))
model.summary()
'''

model = Sequential()
model.add(ConvLSTM2D(16, kernel_size=(3,3), activation='sigmoid',padding='same',input_shape=input_shape,
                     return_sequences=True))
model.add(ConvLSTM2D(16, kernel_size=(3,3), activation='sigmoid',padding='same'))
model.add(Flatten())
model.add(Dense(3, activation='softmax'))
model.summary()

model.compile(loss='categorical_crossentropy', optimizer='adam')

model.fit(X_train, Y_train_, epochs=10, verbose=10, batch_size=32)

x,y = model.evaluate(X_train, Y_train_, verbose=2)
print (x,y)