Doing prediction with RNN on Sequential data in keras

Question

I'm new to the ML and I was following this tutorial which teaches how to do cryptocurrency predictions based on some futures.

My code to do the prediction:

model = load_model("Path//myModel.model")

ready_x = preprocess_df(main_df) # the function returns array of price sequences and targets (0-buy,1-sells): return np.array(X), y 
predictions = []

for x in ready_x:
 l_p = model.predict_classes(x) #error occurs on this line
 predictions.append(l_p[0])
plot_prediction(main_df, predictions)

But i got the below error:

ValueError: Error when checking input: expected lstm_input to have 3 dimensions, but got array with shape (69188, 1)

I don't really get the idea of this error, It's literally my second project on ML after famous cats and dogs classification. So don't have much experience for debugging, I did learn the theory first, about neurons and the relationships between them but still It's really difficult to apply that knowledge to the real project. So the idea of this project is to predict the future price, 3 minute into the future, based on the last 60 minute prices (trained on that).
The model looks like this:

model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]),return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.1))
model.add(BatchNormalization())

model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(32, activation="relu"))
model.add(Dropout(0.2))

model.add(Dense(2, activation="softmax"))

opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)  

main_df is a data frame and consists of:

My question is, how i should I feed the model with correct data input to do this prediction?

EDIT:
preprocess function:

def preprocess_df(df):
    #scalling
    df = df.drop('future', 1)

    for col in df.columns:
        if col!= "target":
            df[col] = df[col].pct_change() # normalizes the data
            df.dropna(inplace=True)
            df[col] = preprocessing.scale(df[col].values) #scale the data between 0-1

    df.dropna(inplace=True)

    sequential_data = []
    prev_days = deque(maxlen=SEQ_LEN)

    for i in df.values:
        prev_days.append([n for n in i[:-1]]) # append each column and not taking a target
        if len(prev_days) == SEQ_LEN:
            sequential_data.append([np.array(prev_days), i[-1]])

    random.shuffle(sequential_data)

    # BALANCING THE DATA
    buys = []
    sells = []

    for seq, target in sequential_data:
        if target == 0:
            sells.append([seq, target])
        elif target == 1:
            buys.append([seq, target])

    random.shuffle(buys)
    random.shuffle(sells)

    lower = min(len(buys), len(sells))

    buys = buys[:lower]
    sells = sells[:lower]

    sequential_data = buys + sells
    random.shuffle(sequential_data)

    X = []
    y = []

    for seq, target in sequential_data:
        X.append(seq)
        y.append(target)

    return np.array(X), y

Answer 1

LSTM expects inputs shaped (batch_size, timesteps, channels); in your case, timesteps=60, and channels=128. batch_size is how many samples you're feeding at once, per fit / prediction.

Your error indicates preprocessing flaws:

• Rows of your DataFrame, based on index name time, would fill dim 1 of x -> timesteps
• Columns are usually features, and would fill dim 2 of x -> channels
• dim 0 is the samples dimension; a "sample" is an independent observation - depending on how your data is formatted, one file could be one sample, or contain multiple

Once accounting for above:

• print(x.shape) should read (N, 60, 128), where N is the number of samples, >= 1
• Since you're iterating over ready_x, x will slice ready_x along its dim 0 - so print(ready_x.shape) should read (M, N, 60, 128), where M >= 1; it's the "batches" dimension, each slice being 1 batch.

As basic debugging: insert print(item.shape) throughout your preprocessing code, where item is an array, DataFrame, etc. - to see how shapes change throughout various steps. Ensure that there is a step which gives 128 on the last dimension, and 60 on second-to-last.