Keras correct input shape for multilayer perceptron

Question

I'm trying to make a basic MLP example in keras. My input data has the shape train_data.shape = (2000,75,75) and my testing data has the shape test_data.shape = (500,75,75). 2000 and 500 are the numbers of samples of training and test data (in other words, the shape of the data is (75,75), but there are 2000 and 500 pieces of training and testing data). The output should have two classes.

I'm unsure what value to use for the input_shape parameter on the first layer of the network. Using the code from the mnist example in the keras repository, I have (updated):

from six.moves    import cPickle
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils  import np_utils
from keras.optimizers import RMSprop

# Globals
NUM_CLASSES = 2
NUM_EPOCHS  = 10
BATCH_SIZE  = 250

def loadData():
    fData = open('data.pkl','rb')
    fLabels = open('labels.pkl','rb')
    data = cPickle.load(fData)
    labels = cPickle.load(fLabels)

    train_data = data[0:2000]
    train_labels = labels[0:2000]
    test_data = data[2000:]
    test_labels = labels[2000:]
    return (train_data, train_labels, test_data, test_labels)

# Load data and corresponding labels for model
train_data, train_labels, test_data, test_labels = loadData()

train_labels = np_utils.to_categorical(train_labels, NUM_CLASSES)
test_labels  = np_utils.to_categorical(test_labels, NUM_CLASSES)

print(train_data.shape)
print(test_data.shape)

model = Sequential()
model.add(Dense(512, input_shape=(5625,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(2))
model.add(Activation('softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

history = model.fit(train_data, train_labels, validation_data=(test_data, test_labels), 
                    batch_size=BATCH_SIZE, nb_epoch=NUM_EPOCHS,
                    verbose=1)
score = model.evaluate(test_data, test_labels, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

where 5625 is 75 * 75 (emulating the MNIST example). The error I get is:

Error when checking model input: expected dense_input_1 to have 2 dimensions, but got array with shape (2000, 75, 75)

Any ideas?

Brochu

Can you show more of your code? Specifically including that line that gives you that error? You mention two variables train_data and test_data, but I don't see them used in the code so I'm not sure how they would contribute to the situation you find yourself in. Is there a full, minimal, code sample that generates this error? – Sail

you might just need to reshape test_data and train_data to match 5625 , with X.reshape(-1,75*75) – Mandymandych

Answer 1

From keras MLP example, https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)

And the model input

model = Sequential()
model.add(Dense(512, input_shape=(784,)))

So you should reshape your train and test to (2000,75*75) and (500,75*75) with

train_data = train_data.reshape(2000, 75*75)
test_data = test_data.reshape(500, 75*75)

and then set the model input shape as you did

model.add(Dense(512, input_shape=(75*75,)))

Plasmodium