Problem with making predictions

[vasilevskykv]

Hello!

I try to use your generator for my case, where:
lookback = 1440 = 5 days (timeframe = 5 minutes)
step =1, i.e. my observations will be sampled at one data point per 5 minutes (is it correct?).

df = pd.read_csv('data/USDT_BTC.csv')
df.columns = ['date','high','low','open','close','volume','quoteVolume','weightedAverage']
df['ema'] = df.close.ewm(span=14).mean()

data_dir = ''
fname = os.path.join(data_dir, 'data/USDT_BTC.csv')

float_data = df['ema'].to_numpy()
mean = float_data[:200000].mean(axis=0)
float_data -= mean
std = float_data[:200000].std(axis=0)
float_data /= std

def generator(data, lookback, delay, min_index, max_index,
              shuffle=False, batch_size=128, step=6):
    if max_index is None:
        max_index = len(data) - delay - 1
    i = min_index + lookback
    while 1:
        if shuffle:
            rows = np.random.randint(
                min_index + lookback, max_index, size=batch_size)
        else:
            if i + batch_size >= max_index:
                i = min_index + lookback
            rows = np.arange(i, min(i + batch_size, max_index))
            i += len(rows)

        samples = np.zeros((len(rows),
                           lookback // step,
                           data.shape[-1]))
        targets = np.zeros((len(rows),))
        for j, row in enumerate(rows):
            indices = range(rows[j] - lookback, rows[j], step)
            samples[j] = data[indices]
            targets[j] = data[rows[j] + delay][1]
        yield samples, targets

lookback = 1440
step = 1
delay = 0
batch_size = 128

train_gen = generator(float_data,
                      lookback=lookback,
                      delay=delay,
                      min_index=0,
                      max_index=200000,
                      shuffle=True,
                      step=step, 
                      batch_size=batch_size)
val_gen = generator(float_data,
                    lookback=lookback,
                    delay=delay,
                    min_index=200001,
                    max_index=300000,
                    step=step,
                    batch_size=batch_size)
test_gen = generator(float_data,
                     lookback=lookback,
                     delay=delay,
                     min_index=300001,
                     max_index=None,
                     step=step,
                     batch_size=batch_size)

val_steps = (300000 - 200001 - lookback) // batch_size
test_steps = (len(float_data) - 300001 - lookback) // batch_size
model = Sequential()
model.add(layers.Flatten(input_shape=(lookback // step, float_data.shape[-1])))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(1))

model.compile(optimizer=RMSprop(), loss='mae')
history = model.fit_generator(train_gen,
                              steps_per_epoch=500,
                              epochs=20,
                              validation_data=val_gen,
                              validation_steps=val_steps)

I got an error:
2020-12-24 16:58:21.666986: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at random_op.cc:74 : Resource exhausted: OOM when allocating tensor with shape[605924640,32] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc

Besides I would like to know an example of model.predict in this case.
And one more question:
What is the way to appky the model

model = Sequential()
model.add(layers.Embedding(max_features, 128))
model.add(layers.LSTM(32))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])
history = model.fit(x_train, y_train,
                    epochs=10,
                    batch_size=128,
                    validation_split=0.2)

to my case?

[keremtatlici]

try to decrease batch_size, it should solve your problem. And did you find how to make prediction?i need that too