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'))