def pure_temp_prediction(): X = diff_length_csv('tiwencheck.csv') X = np.array(X) y = fun2('number_category.csv') X_train, X_test, y_train, y_test = cross_validation.train_test_split( X, y, test_size=0.2) y_train = category_to_target(y_train) y_test = category_to_target(y_test) x_train = reshape_dataset(X_train) x_test = reshape_dataset(X_test) model = Sequential() model.add(LSTM(16, input_shape=(12, 1))) model.add(Dropout(0.25)) model.add(Dense(nb_classes, bias=False)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() print('Train...') model.fit(x_train, y_train, batch_size=batch_size, nb_epoch=5, validation_split=0.05) score, acc = model.evaluate(x_test, y_test, batch_size=batch_size) print(model.predict_proba(reshape_dataset(X))) print('Test score:', score) print('Test accuracy:', acc)
def length_temp_prediction(): X = diff_length_csv('5_day_50_check.csv') # X=fun2('5_day_50_check.csv') # X = pad_sequences(X, maxlen=maxlen, padding='post', truncating='post', dtype='float') X = np.array(X, dtype=float) y = fun2('number_category.csv') X_train, X_test, y_train, y_test = cross_validation.train_test_split( X, y, test_size=0.2) y_train = category_to_target(y_train) y_test = category_to_target(y_test) x_train = reshape_dataset(X_train) x_test = reshape_dataset(X_test) model = Sequential() # model.add(Masking(mask_value=0, input_shape=(maxlen, 1))) model.add(LSTM(3, input_shape=(maxlen, 1), return_sequences=True)) model.add(Dropout(0.25)) model.add(Reshape((maxlen * 3, ))) model.add(Dense(nb_classes, b_regularizer=l1(0.01))) model.add(Dropout(0.25)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() print('Train...') model.fit(x_train, y_train, batch_size=batch_size, nb_epoch=30, validation_split=0.05) score, acc = model.evaluate(x_test, y_test, batch_size=batch_size) print(model.predict_proba(reshape_dataset(X))) print('Test score:', score) print('Test accuracy:', acc)
# for i in range(0,maxlen-1): # y_train=np.concatenate((y_train_temp,y_train),axis=1) # y_test=np.concatenate((y_test_temp,y_test),axis=1) # print(y_train.shape) # x_train represents list temperature # x2_train represents test parameter x_train = X_train[:, in_file_length:] x2_train = X_train[:, 0:len(X2[0])] x_test = X_test[:, in_file_length:] x2_test = X_test[:, 0:len(X2[0])] print('X_train shape:', x_train.shape) print('X_test shape:', x_test.shape) x_train = reshape_dataset(x_train) x_test = reshape_dataset(x_test) print('X_train shape:', x_train.shape) print('X_test shape:', x_test.shape) model1 = Sequential() # model1.add(Masking(mask_value=0, input_shape=(maxlen, 1))) model1.add( LSTM(output_dim=5, input_shape=(maxlen, 1), return_sequences=True)) model1.add(Dropout(0.25)) model1.add(Reshape((maxlen * 5, ))) model2 = Sequential() model2.add(Dense(64, input_dim=in_file_length)) model2.add(Dropout(0.25)) model2.add(Activation('relu'))