model = (TimeDistributed(Conv2D(256, (3, 3), strides=(1, 1), activation='relu', padding='same')))(model) model = (TimeDistributed(Conv2D(256, (3,3), activation='relu')))(model) model = (TimeDistributed(MaxPooling2D((2, 2), strides=(1, 1))))(model) model = (Dropout(0.25))(model) model = (TimeDistributed(Flatten()))(model) model = (Dropout(0.5))(model) #AFTER LSTM model = LSTM(128, return_sequences=True)(model) model = attention_3d_block(model) model = Flatten()(model) model = (Dense(6, activation='softmax'))(model) model = Model(input = x, output = model) opt = keras.optimizers.Adam(lr=0.0005) print(model.summary()) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) ## filepath="weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5" ## checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='auto') ## callbacks_list = [checkpoint] #mm = ModelCheckpoint('/user1/temp/cscr/pooja_t/', monitor='loss', verbose=0, save_best_only=True, mode='auto', period=1) checkpoint = EarlyStopping(monitor='loss', min_delta=0, patience=10, verbose=0, mode='auto') callbacks_list = [checkpoint] model.fit(X_train, y_train, batch_size=32, epochs=70, verbose=1, callbacks=callbacks_list, validation_split=0.1) #model.fit(X_train, y_train, batch_size=32, epochs=100, verbose=1) #model.load_weights('C:\\Users\\Sanmoy\\Desktop\\pooja\\paper read\\sports\\dataset\\UIUC2\\B128-256.h5') score = (model.evaluate(X_test,y_test)) print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100)) model.save('/user1/temp/cscr/pooja_t/after64.h5')