check = ModelCheckpoint("weights-model-06.{epoch:02d}-{val_acc:.5f}.hdf5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='auto')
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[check],
validation_data=(x_test, y_test))
## Predict
print('Predictions...')
x_test = test_load()
x_test = x_test.astype('float32')
x_test /= 255.
best_threshold = [0.2] * 17
# print("best_threshold: {}".format(best_threshold))
pred = model.predict(x_test, verbose=1)
# print(pred)
print(pred.shape)
classes = [
'haze', 'primary', 'agriculture', 'clear', 'water', 'habitation', 'road',
'cultivation', 'slash_burn', 'cloudy', 'partly_cloudy',
'conventional_mine', 'bare_ground', 'artisinal_mine', 'blooming',
def train_full_model():
# build the VGG16 network
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=input_shape)
print('Model loaded.')
# base_model.summary()
# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=(4, 4, 512)))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(17, activation='sigmoid'))
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)
# top_model.summary()
# add the model on top of the convolutional base
model = Model(inputs=base_model.input,
outputs=top_model(base_model.output))
# set the first 15 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:15]:
# print(layer.name)
layer.trainable = False
model.summary()
model.load_weights('weights-model-07.01-0.95972.hdf5')
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=adam(lr=1e-4),
metrics=['accuracy'])
x_train, x_test, y_train, y_test = load(dataset='train-dataset-128.h5',
test_size=0.3)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# tbCallBack = TensorBoard(log_dir='/Graph', histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=1)
check = ModelCheckpoint("weights-model-07.{epoch:02d}-{val_acc:.5f}.hdf5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='auto')
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[check],
validation_data=(x_test, y_test))
## Predict
print('Predictions...')
x_test = test_load('test-dataset-128.h5')
x_test = x_test.astype('float32')
x_test /= 255.
best_threshold = [0.2] * 17
# print("best_threshold: {}".format(best_threshold))
pred = model.predict(x_test, verbose=1, batch_size=8)
# print(pred)
print(pred.shape)
classes = [
'haze', 'primary', 'agriculture', 'clear', 'water', 'habitation',
'road', 'cultivation', 'slash_burn', 'cloudy', 'partly_cloudy',
'conventional_mine', 'bare_ground', 'artisinal_mine', 'blooming',
'selective_logging', 'blow_down'
]
y_pred = []
##text=List of strings to be written to file
with open(submission_file, 'w') as file:
file.write("image_name,tags")
file.write('\n')
for i in range(pred.shape[0]):
y_pred = np.array([
1 if pred[i, j] >= best_threshold[j] else 0
for j in range(pred.shape[1])
])
# print(y_pred)
# extracting actual class name
y_pred = [classes[i] for i in range(17) if y_pred[i] == 1]
y_pred = " ".join([str(item) for item in y_pred])
# print(y_pred)
line = "test_{},{}".format(i, y_pred)
file.write(line)
file.write('\n')