Keras time series output dense more than one not work
0
I want to predict timeseries 6 month in one time. I set model.add(Dense(6)) but it show error like this .
ValueError: Error when checking input: expected dense_1_input to have
shape (6,) but got array with shape (1,)
This is my code.
df = pd.read_csv('D://data.csv',
engine='python')
df['DATE_'] = pd.to_datetime(df['DATE_']) + MonthEnd(1)
df = df.set_index('DATE_')
df.head()
split_date = pd.Timestamp('03-01-2015')
train = df.loc[:split_date, ['data']]
test = df.loc[split_date:, ['data']]
sc = MinMaxScaler()
train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)
X_train = train_sc[:-1]
y_train = train_sc[1:6]
X_test = test_sc[:-1]
y_test = test_sc[1:6]
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=6, activation='relu'))
model.add(Dense(6))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
model.fit(X_train, y_train, epochs=200, batch_size=2)
y_pred = model.predict(X_test)
real_pred = sc.inverse_transform(y_pred)
real_test = sc.inverse_transform(y_test)
print("Predict Value")
print(real_pred)
print("Test Value")
print(real_test)
python keras time-series lstm
asked Nov 12 '18 at 17:51
user58519user58519
224
add a comment |
0
I want to predict timeseries 6 month in one time. I set model.add(Dense(6)) but it show error like this .
ValueError: Error when checking input: expected dense_1_input to have
shape (6,) but got array with shape (1,)
This is my code.
df = pd.read_csv('D://data.csv',
engine='python')
df['DATE_'] = pd.to_datetime(df['DATE_']) + MonthEnd(1)
df = df.set_index('DATE_')
df.head()
split_date = pd.Timestamp('03-01-2015')
train = df.loc[:split_date, ['data']]
test = df.loc[split_date:, ['data']]
sc = MinMaxScaler()
train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)
X_train = train_sc[:-1]
y_train = train_sc[1:6]
X_test = test_sc[:-1]
y_test = test_sc[1:6]
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=6, activation='relu'))
model.add(Dense(6))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
model.fit(X_train, y_train, epochs=200, batch_size=2)
y_pred = model.predict(X_test)
real_pred = sc.inverse_transform(y_pred)
real_test = sc.inverse_transform(y_test)
print("Predict Value")
print(real_pred)
print("Test Value")
print(real_test)
python keras time-series lstm
asked Nov 12 '18 at 17:51
user58519user58519
224
add a comment |
0
0
0
I want to predict timeseries 6 month in one time. I set model.add(Dense(6)) but it show error like this .
ValueError: Error when checking input: expected dense_1_input to have
shape (6,) but got array with shape (1,)
This is my code.
df = pd.read_csv('D://data.csv',
engine='python')
df['DATE_'] = pd.to_datetime(df['DATE_']) + MonthEnd(1)
df = df.set_index('DATE_')
df.head()
split_date = pd.Timestamp('03-01-2015')
train = df.loc[:split_date, ['data']]
test = df.loc[split_date:, ['data']]
sc = MinMaxScaler()
train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)
X_train = train_sc[:-1]
y_train = train_sc[1:6]
X_test = test_sc[:-1]
y_test = test_sc[1:6]
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=6, activation='relu'))
model.add(Dense(6))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
model.fit(X_train, y_train, epochs=200, batch_size=2)
y_pred = model.predict(X_test)
real_pred = sc.inverse_transform(y_pred)
real_test = sc.inverse_transform(y_test)
print("Predict Value")
print(real_pred)
print("Test Value")
print(real_test)
python keras time-series lstm
asked Nov 12 '18 at 17:51
user58519user58519
224
I want to predict timeseries 6 month in one time. I set model.add(Dense(6)) but it show error like this .
ValueError: Error when checking input: expected dense_1_input to have
shape (6,) but got array with shape (1,)
This is my code.
df = pd.read_csv('D://data.csv',
engine='python')
df['DATE_'] = pd.to_datetime(df['DATE_']) + MonthEnd(1)
df = df.set_index('DATE_')
df.head()
split_date = pd.Timestamp('03-01-2015')
train = df.loc[:split_date, ['data']]
test = df.loc[split_date:, ['data']]
sc = MinMaxScaler()
train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)
X_train = train_sc[:-1]
y_train = train_sc[1:6]
X_test = test_sc[:-1]
y_test = test_sc[1:6]
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=6, activation='relu'))
model.add(Dense(6))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
model.fit(X_train, y_train, epochs=200, batch_size=2)
y_pred = model.predict(X_test)
real_pred = sc.inverse_transform(y_pred)
real_test = sc.inverse_transform(y_test)
print("Predict Value")
print(real_pred)
print("Test Value")
print(real_test)
python keras time-series lstm
python keras time-series lstm
asked Nov 12 '18 at 17:51
user58519user58519
224
asked Nov 12 '18 at 17:51
user58519user58519
224
asked Nov 12 '18 at 17:51
user58519user58519
224
asked Nov 12 '18 at 17:51
user58519user58519
224
asked Nov 12 '18 at 17:51
user58519user58519
224
224
add a comment |
add a comment |
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