Struggling to setup a basic LSTM based on numpy array input - python-3.x

Question

I am trying to setup an LSTM in order to feed it with my numpy array features and labels.
Here is my first attempt:
nb_features =len(seq_cols)
print("initial shape:", X_train.shape)
print("nb features", nb_features)
# X_train = X_train.reshape(X_train.shape + (1,))
print("Seq length ", seq_length)
print('New shape ', X_train.shape)
model = Sequential()
model.add(LSTM(
input_shape=(nb_features, 1),
units=100,
return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
Which gives me the output
initial shape: (175850, 4)
nb features 4
Seq length 50
New shape (175850, 4)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-99-50959413cb62> in <module>()
1 model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
----> 2 callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
2 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
126 ': expected ' + names[i] + ' to have ' +
127 str(len(shape)) + ' dimensions, but got array '
--> 128 'with shape ' + str(data_shape))
129 if not check_batch_axis:
130 data_shape = data_shape[1:]
ValueError: Error when checking input: expected lstm_28_input to have 3 dimensions, but got array with shape (175850, 4)
So I am trying to reshape by uncommenting line 4
nb_features =len(seq_cols)
print("initial shape:", X_train.shape)
print("nb features", nb_features)
X_train = X_train.reshape(X_train.shape + (1,))
print("Seq length ", seq_length)
print('New shape ', X_train.shape)
model = Sequential()
model.add(LSTM(
input_shape=(nb_features, 1),
units=100,
return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
which now gives me the error that I have wrong dimensions.
initial shape: (175850, 4)
nb features 4
Seq length 50
New shape (175850, 4, 1)
ValueError: Error when checking input: expected lstm_28_input to have 3 dimensions, but got array with shape (175850, 4)
I think I am just wondering around doing random changes.
Can anyone please give me an insight on what pieces I am missing from the puzzle? I am a beginner in the field and the errors are not helping me much.
P.S: X_train is a numpy array

Answer 1

Keras input_shape ignores the first dimension because it indicates the number of training examples, m. This is because Keras is able to work with any number of training examples, it only cares about the actual input dimensions.
For example, input_shape=(nb_features, 1)=(4,1) means it is expecting the input to be (None, 4, 1), where none is the number of training examples. You can also see this by typing model.summary() after you compile, but before you fit.
This is 3 dimensions, hence the "expected lstm_28_input to have 3 dimensions" error. You're feeding it (175850, 4) which is a two dimensional array.