I am stacking 6 layers of 2D satellite imagery (x data) and attempting to run a CNN over them to classify the landcover (using 8 land cover classes taken from a reformatted USDA Crop Data Layer - y data).
The x data is shaped (2004, 2753, 6) and the y is shaped (2004, 2753, 8) originally and I have used data_x.reshape(-1,2004,2752,6) (same for y) to add an extra dimension as the model.
The 8 categories in the y data-set represent 8 possible land-cover categories in numerical format in 8 bands (i.e. 1st band is corn and represented by 1's for positive and 0 for not corn).
However, when i try to run the model the expected shape does not match what is being passed through to it. I am unsure if I am using the correct model structure or data structure - one idea would be to take the 8 bands of the y dataset
Based on some serious googling i have been learning how to get the data into the correct format with the right number of dimensions etc but feel I am falling at the last hurdle with regards to dimensions (and most likely correct preparation of the x & y data sets).
Below is the CNN model
input_shape=([2004, 2753, 6])
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),strides=(1, 1),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2), padding="same"))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), padding="same"))
model.add(Dropout(0.25))
#model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(8, activation='softmax'))
#model.add(Flatten())
model.summary()
Model Summary - expecting 500, 687, 8 out at the end
Layer (type) Output Shape Param #
=================================================================
conv2d_54 (Conv2D) (None, 2002, 2751, 32) 1760
_________________________________________________________________
max_pooling2d_52 (MaxPooling (None, 1001, 1376, 32) 0
_________________________________________________________________
conv2d_55 (Conv2D) (None, 999, 1374, 32) 9248
_________________________________________________________________
max_pooling2d_53 (MaxPooling (None, 500, 687, 32) 0
_________________________________________________________________
dropout_57 (Dropout) (None, 500, 687, 32) 0
_________________________________________________________________
dense_59 (Dense) (None, 500, 687, 128) 4224
_________________________________________________________________
dropout_58 (Dropout) (None, 500, 687, 128) 0
_________________________________________________________________
dense_60 (Dense) (None, 500, 687, 8) 1032
=================================================================
Total params: 16,264
Trainable params: 16,264
Non-trainable params: 0
_________________________________________________________________
compile
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
fit - and where i get the error message
history = model.fit(x_train3d, y_train3d,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.2, validation_data=None)
shape of x_train3D = (1, 2004, 2753, 6) shape of y_train3D = (1, 2004, 2753, 8)
error message
ValueError: Error when checking target: expected dense_58 to have shape (500, 687, 8) but got array with shape (2004, 2753, 8)
Again, I suspect this is down to needing to get the data in the right format both for the input and output but also likely something wrong in the specification of the model. Would appreciate some guidance as i'm new to Keras.
