CODEWITHSUNDEEP
machine-learningcomputer-visionkeras
JamesD
JamesD

Reputation: 611

Cannot create keras model while using flow_from_directory

I am trying to create a model to fit data from the cifar-10 dataset. I have a working convolution neural network from an example but when I try to create a multi layer perceptron I keep getting a shape mismatch problem.

#https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
#https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html


from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.optimizers import RMSprop


# dimensions of our images.
img_width, img_height = 32, 32

train_data_dir = 'pro-data/train'
validation_data_dir = 'pro-data/test'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=input_shape))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

model.summary()

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

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator()

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

score = model.evaluate_generator(validation_generator, 1000)
print("Accuracy = ", score[1])

The error I am getting is this:

ValueError: Error when checking target: expected dense_3 to have 4 dimensions, but got array with shape (16, 1)

But if if change the input_shape for the input layer to an incorrect value "(784,)", I get this error:

ValueError: Error when checking input: expected dense_1_input to have 2 dimensions, but got array with shape (16, 32, 32, 3)

This is where I got a working cnn model using flow_from_directory: https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d

In case anyone is curious I am getting an accuracy of only 10% for cifar10 using the convolution neural network model. Pretty poor I think.

Upvotes: 0

Views: 688

Answers (1)

nikhalster
nikhalster

Reputation: 481

according to your model, your model summary is

dense_1 (Dense) (None, 32, 32, 512) 2048

dropout_1 (Dropout) (None, 32, 32, 512) 0

dense_2 (Dense) (None, 32, 32, 512) 262656

dropout_2 (Dropout) (None, 32, 32, 512) 0

dense_3 (Dense) (None, 32, 32, 10) 5130

Total params: 269,834

Trainable params: 269,834

Non-trainable params: 0

Your output format is (32,32,10)

In the cifar-10 dataset you want to classify into 10 labels

Try adding

model.add(Flatten())

before your last dense layer.

Now your output layer is

Layer (type) Output Shape Param #

dense_1 (Dense) (None, 32, 32, 512) 2048

dropout_1 (Dropout) (None, 32, 32, 512) 0

dense_2 (Dense) (None, 32, 32, 512) 262656

dropout_2 (Dropout) (None, 32, 32, 512) 0

flatten_1 (Flatten) (None, 524288) 0

dense_3 (Dense) (None, 10) 5242890

Total params: 5,507,594

Trainable params: 5,507,594

Non-trainable params: 0

Also, you've just used the dense and dropout layers in your model. To get better accuracy you should google the various CNN architectures which consists of dense and maxpooling layers.

Upvotes: 1

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