Why do I get a Keras LSTM RNN input_shape error?

I keep getting an input_shape error from the following code.

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM

def _load_data(data):
    """
    data should be pd.DataFrame()
    """
    n_prev = 10
    docX, docY = [], []
    for i in range(len(data)-n_prev):
        docX.append(data.iloc[i:i+n_prev].as_matrix())
        docY.append(data.iloc[i+n_prev].as_matrix())
    if not docX:
        pass
    else:
        alsX = np.array(docX)
        alsY = np.array(docY)
        return alsX, alsY

X, y = _load_data(dframe)
poi = int(len(X) * .8)
X_train = X[:poi]
X_test = X[poi:]
y_train = y[:poi]
y_test = y[poi:]

input_dim = 3

All of the above runs smoothly. This is where it goes wrong.

in_out_neurons = 2
hidden_neurons = 300
model = Sequential()
#model.add(Masking(mask_value=0, input_shape=(input_dim,)))
model.add(LSTM(in_out_neurons, hidden_neurons, return_sequences=False, input_shape=(len(full_data),)))
model.add(Dense(hidden_neurons, in_out_neurons))
model.add(Activation("linear"))
model.compile(loss="mean_squared_error", optimizer="rmsprop")
model.fit(X_train, y_train, nb_epoch=10, validation_split=0.05)

It returns this error.

Exception: Invalid input shape - Layer expects input ndim=3, was provided with input shape (None, 10320)

When I check the website it says to specify a tuple "(e.g. (100,) for 100-dimensional inputs)."

That being said, my data set consists of one column with a length of 10320. I assume that that means that I should be putting (10320,) in as the input_shape, but I get the error anyways. Does anyone have a solution?