I am trying to write a pretty complicated neural network (at least for me) in keras that needs to combine both a common CNN structure and an LSTM/GRU layer.
Basically, I have a dataset of climatological maps of the Mediterranean sea, each map details the wind, pressure and other parameters. I am studying Medicanes (Mediterranean hurricanes) and my goal is to create a neural network that can classify each map with a label zero if there is no trace of such hurricanes or one if the map contains one.
In order to achieve that I need a network with two parts:
- feature extractor (normal CNN).
- temporal layer (LSTM/GRU).
The main cause of this is that each map is correlated with the previous one because the formation and life cycle of a Medicane can take several days to complete.
Important note: the dataset is too big to be uploaded all at once so I have to work one batch at a time.
I am working with Keras and I found it pretty challenging to adapt its standard framework to my needs so I have come up with some peculiar flow to feed my data into the network.
In particular, I found it hard to pass both my batch size and my time-step parameter to the GRU layer using a more standard alternative.
This is what I tried:
I am positively sure I have overcomplicated the task, but, as I said I am not very proficient with Keras and TensorFlow.
The main problem was that I could not find a way to import the data both in a batch (for RAM reasons) and in a sequence of 10-15 pictures (to be used as the time steps in the GRU layer).
I solved this problem by importing batches of 120 maps in order (no shuffle) and I created a way to turn these batches into the sequence of images I needed then I proceeded to re-batch the sequences and feed them to the model manually.
Data Import
batch_size=120
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"./Figures_1/Train",
validation_split=None,
subset=None,
labels="inferred",
label_mode="binary",
color_mode="rgb",
interpolation='bilinear',
batch_size=batch_size,
image_size=(600, 600),
shuffle=False,
seed=123
)
Get a sequence of Images
Here, I break down the 120 map batches into sequences of 60 observations, and I return each sequence one at a time.
sequence_lengh=60
def sequence_x(train_dataset):
x_numpy = np.asarray(list(map(lambda x: x[0], tfds.as_numpy(train_dataset))),dtype=object)
for element in range(0,x_numpy.shape[0]):
for i in range(0, x_numpy.shape[0],sequence_lengh):
x_seq = x_numpy[element][i:i+sequence_lengh]
yield x_seq
def sequence_y(train_dataset):
y_numpy = np.asarray(list(map(lambda x: x[1], tfds.as_numpy(train_dataset))),dtype=object)
for element in range(0,y_numpy.shape[0]):
for i in range(0, y_numpy.shape[0],sequence_lengh):
y_seq = y_numpy[element][i:i+sequence_lengh]
yield y_seq
CNN Model
I build the CNN model based on a pre-trained DenseNet
from keras.layers import TimeDistributed, GRU
def build_convnet(shape=(600, 600, 3)):
inputs = keras.Input(shape = shape)
x = inputs
# preprocessing
x = keras.applications.densenet.preprocess_input(x)
#Convbase
x = convBase(x)
x = layers.Flatten()(x)
# Fine tuning
x = keras.layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = keras.layers.Dense(512, activation='relu')(x)
x = keras.layers.GlobalMaxPool2D()
return x
GRU Model
I build the time part of the network with a GRU layer
def action_model(shape=(15, 600, 600, 3), nbout=15):
# Create our convnet with (112, 112, 3) input shape
convnet = build_convnet(shape[1:]) #[1:]
# then create our final model
model = keras.Sequential()
# add the convnet with (5, 112, 112, 3) shape
model.add(TimeDistributed(convnet, input_shape=shape))
# here, you can also use GRU or LSTM
model.add(GRU(64))
# and finally, we make a decision network
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(64, activation='relu'))
model.add(Dense(15, activation='softmax'))
return model
Transfer Learning
I retrain a part of the GRU
convBase = DenseNet121(include_top=False, weights=None, input_shape=(600,600,3), pooling="avg")
for layer in convBase.layers:
if 'conv5' in layer.name:
layer.trainable = True
for layer in convBase.layers:
if 'conv4' in layer.name:
layer.trainable = True
Model Compile
Model compilation ( image size= 600x600x3)
INSHAPE=(15, 600, 600, 3) # (5, 112, 112, 3)
model = action_model(INSHAPE, 1)
optimizer = keras.optimizers.Adam(0.001)
model.compile(
optimizer,
'categorical_crossentropy',
metrics='accuracy'
)
Model Fit
Here I manually batch my data. I turn an array (60, 600, 600, 3) into a (4,15,600,600) array. Meaning 4 batches each one containing a 15-map long sequence.
epochs = 10
for value in range(0, epochs):
train_x, train_y = sequence_x(train_ds), sequence_y(train_ds)
val_x, val_y = sequence_x(validation_ds), sequence_y(validation_ds)
for i in range(0,278): #
x = next(train_x, "none")
y = next(train_y, "none")
if (x!="none" or y!="none"):
if (np.any(x) and np.any(y)):
x_stack = np.stack((x[:15], x[15:30], x[30:45], x[45:]))
y_stack = np.stack((y[:15], y[15:30], y[30:45], y[45:]))
y_stack=y_stack.reshape(4,15)
model.fit(x=x_stack, y=y_stack,
validation_data=None,
batch_size=None,
shuffle=False
)
else:
continue
else:
continue
The idea is to get a model that, when presented with a sequence of images, can categorize each one of them with a 0 or a 1 if they have a Medicane or not.
The model does compile without any errors but the results it provides are horrible:
.
What am I doing incorrectly? Is there a more effective way to write all of this?
