model = (Dense(32, activation='relu'))(merge)
model = Dropout(0.5)(model)
model = (Dense(1, activation='sigmoid'))(model)
model = Model(inputs=[energy_input, maximum_amplitude_input, radial_strain_input, tangential_strain_input],
outputs=model)
model.compile(loss='binary_crossentropy', optimizer="rmsprop", metrics=['accuracy'])
print model.summary()
print "Training....................................................."
model.fit([energies, maximum_amplitudes, radial_strains, tangential_strains], train_labels, epochs=epochs, verbose=1,
batch_size=batch_size, validation_split=0.2)
print("--- train %s seconds ---" % (time.time() - start_time))
original_classified = model.predict([energies, maximum_amplitudes, radial_strains, tangential_strains],
batch_size=batch_size, verbose=0)
best_threshold = find_best_threshold(train_labels, original_classified)
if run_test:
print 'test'
for test in test_sets:
print test
test_reader = pd.read_csv(get_diff_path(test))
energy_test, maximum_amplitude_test, radial_strain_test, tangential_strain_test, test_labels = read_data(
test_reader)
original_classified = model.predict(
[energy_test, maximum_amplitude_test, radial_strain_test, tangential_strain_test], batch_size=batch_size,
verbose=0)
print "0.5 threshold"
# plt.ylabel('accuracy')
# plt.show()
#Final evaluation of the model
score_val = model.evaluate(x_valid, y_valid, verbose=0)
print("Accuracy on Validation data: %.2f%%" % (score_val[1] * 100))
score_train = model.evaluate(x_train, y_train, verbose=0)
print("Accuracy on Train data: %.2f%%" % (score_train[1] * 100))
test_files = []
predictions = []
correct = []
print('Predicting test data ...')
for file_names, spectrograms, target in test_data_processing():
predicts = model.predict(spectrograms)
predicts = np.argmax(predicts, axis=1)
predicts = [class_labels[p] for p in predicts]
test_files.extend(file_names)
predictions.extend(predicts)
correct.extend(target)
number_of_corrects = 0
for x in range(len(predictions)):
if (np.array_equal(correct[x], predicts[x])):
number_of_corrects += 1
score_test = number_of_corrects / len(predictions)
print("Accuracy on Test data: %.2f%%" % (score_test * 100))
print('Saving predictions to csv ...')
df = pd.DataFrame(columns=['file_name', 'predicted_label'])
# ### Uncomment the next cell for training
# In[ ]:
#m.fit(X_train, Y_train, epochs = 350,validation_data=(X_valid,Y_valid),callbacks=[checkpoint,rlStop],
# batch_size=batch_size, verbose = 2)
# In[28]:
#m.load_weights('/output/CNN_stacked_w2v.hdfs')
pred = m.predict(X_test,batch_size=batch_size)
acc_test = accuracy_score(Y_test.argmax(axis=1),pred.argmax(axis=1))
print('Model2 accuracy' + str(acc_test))
# In[38]:
#Model7
batch_size = 64
n_bilstm_1 = 8
n_bilstm_2 = 16
drop_bilstm = 0.25
embed_dim = 512
input_layer = Input(shape=(max_tweet_length,))
embedding_layer = Embedding(max_features, embed_dim)(input_layer)
if (0):
model3 = load_model(os.path.join(root_folder,
r"cnn_models/roi_test.h5"),
custom_objects={"RoI": RoI})
image = np.array(input_image[0])
image = np.expand_dims(image, axis=-1)
image = np.expand_dims(image, axis=0)
roi_in = np.array(input_roi[0])
roi_in = np.expand_dims(roi_in, axis=0)
predictions = model3.predict([image, roi_in])
print(predictions)
if (0):
conv = load_model(os.path.join(root_folder,
r"cnn_models/conv_test.h5"))
for i in range(len(input_image)):
images = conv.predict(
np.expand_dims(np.expand_dims(input_image[i], axis=0),
axis=-1))
images = np.reshape(images, (1, 32, 300, 200))
for image in images[0]:
cv2.imshow('image', input_image[i])
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
#print(total_prec / len(image_locs))
#print(total_recall / len(image_locs))
