batch_size,
w_sam_map=w_sample,
train_callbacks=[checkpoint])
train_losses += tmp_train_losses
val_losses += tmp_val_losses
y_pred = classifier.predict(x_valid)
f2samples = fbeta_score(np.array(y_valid),
y_pred > 0.2,
beta=2,
average='samples')
print("F2 samples = {}".format(f2samples))
# <codecell>
# ## Load Best Weights
# Here you should load back in the best weights that were automatically saved by ModelCheckpoint during training
classifier.load_weights(model_filepath + ".hdf5")
print("Weights loaded")
p_pred = classifier.predict(x_valid)
Y = np.array(y_valid)
thrs = optimise_f2_thresholds(Y, p_pred, y_map)
y_pred = p_pred > thrs[np.newaxis, :]
f2samples = fbeta_score(Y, y_pred, beta=2, average="samples")
print("F2 samples = {}".format(f2samples))
R = recall_score(Y, y_pred, average=None)
P = precision_score(Y, y_pred, average=None)
A = np.equal(Y.astype('bool'), y_pred).sum(axis=0).astype('float') / Y.shape[0]
C = Y.sum(axis=0)
validation_split_size=validation_split_size,
train_callbacks=[checkpoint])
train_losses += tmp_train_losses
val_losses += tmp_val_losses
# <markdowncell>
# ## Load Best Weights
# <markdowncell>
# Here you should load back in the best weights that were automatically saved by ModelCheckpoint during training
# <codecell>
classifier.load_weights("weights.best.hdf5")
print("Weights loaded")
# <markdowncell>
# ## Monitor the results
# <markdowncell>
# Check that we do not overfit by plotting the losses of the train and validation sets
# <codecell>
plt.plot(train_losses, label='Training loss')
plt.plot(val_losses, label='Validation loss')
plt.legend()
classifier = AmazonKerasClassifier()
classifier.add_conv_layer(img_resize)
classifier.add_flatten_layer()
classifier.add_ann_layer(len(y_map))
train_losses, val_losses = [], []
epochs_arr = [20, 5, 5]
learn_rates = [0.001, 0.0001, 0.00001]
for learn_rate, epochs in zip(learn_rates, epochs_arr):
tmp_train_losses, tmp_val_losses, fbeta_score = classifier.train_model(x_train, y_train, learn_rate, epochs,
batch_size, validation_split_size=validation_split_size,
train_callbacks=[checkpoint])
train_losses += tmp_train_losses
val_losses += tmp_val_losses
classifier.load_weights("weights.best.hdf5")
print("Weights loaded")
print (fbeta_score)
del x_train, y_train
gc.collect()
x_test, x_test_filename = data_helper.preprocess_test_data(test_jpeg_dir, img_resize)
# Predict the labels of our x_test images
predictions = classifier.predict(x_test)
del x_test
gc.collect()
x_test, x_test_filename_additional = data_helper.preprocess_test_data(test_jpeg_additional, img_resize)
new_predictions = classifier.predict(x_test)