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I'm trying to use maxpooling as a first layer using keras and I have a problem with the input and output dimensions.

print(x_train.shape)
print(y_train.shape)
(15662, 6)
(15662,)

x_train = np.reshape(x_train, (-1,15662, 6)) 
y_train = label_array.reshape(1, -1)

model = Sequential()
model.add(MaxPooling1D(pool_size = 2 , strides=1, input_shape = (15662,6)))
model.add(Dense(5, activation='relu'))
model.add(Flatten())
model.add(Dense(1, activation='softmax'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size= 32, epochs=1)

After running the model, I get the following error:

ValueError: Error when checking target: expected dense_622 (last layer)
to have shape (1,) but got array with shape (15662,)

I'm doing classification and my target is binary (0,1)
Thank you

Your target should have shape (batch_size, 1) but you are passing an array of shape (1, 15662). It seems like 15662 should be the batch size, in which case x_train should have shape (15662, 6) and y_train should have shape (15662, 1). In this case however, it doesn't make any sense to have a MaxPooling1D layer as the first layer of your model since max pooling requires a 3D input (i.e. shape (batch_size, time_steps, features)). You probably want to leave out the max pooling layer (and the Flatten layer). The following code should work:

# x_train: (15662, 6)
# y_train: (15662,)

model = Sequential()
model.add(Dense(5, activation='relu', input_shape=(6,))) # Note: don't specify the batch size in input_shape
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size= 32, epochs=1)

But it of course depends on what kind of data you have.