validation_split=.3, # split data into .7 train, .3 test each run
epochs=2500, # do this many runs unless stop early
callbacks=callbacks_list) # after each run do this list of things, defined above
# ############################## Predictions vs Actual #######################################
best = pd.Series(history.history['val_accuracy']).idxmax()
print(np.array(history.history['val_accuracy']).max())
# 2
print('')
print('train accuracy',history.history['accuracy'][best])
print('test accuracy',history.history['val_accuracy'][best])
print('\n')
my_model = load_model(filepath)
predictions = my_model.predict(features)
result_dict = {'prediction': list(predictions),
'actual': list(target)}
df_result = pd.DataFrame(result_dict)
df_result['Name'] = corners_data['Name']
df_result['Draft Year'] = corners_data['Draft Year']
df_result['Games'] = corners_data['Games']
df_result['Draft Pos'] = corners_data['Draft Pos']
print(df_result.head(50))
def build_encoder_decoder_inference_from_file(self, model_path, sentence_encoder, include_sentence_encoder=True,
attention=False):
if attention:
model = load_model(model_path, custom_objects={'AttentionGRU': AttentionGRU})
else:
model = load_model(model_path)
return self.build_encoder_decoder_inference(model, sentence_encoder, include_sentence_encoder, attention)
def predict():
model1_dir = G.MOD + 'deep_nodense/temp_model.hdf5'
model2_dir = G.MOD + 'deep_dropout_full/temp_model.hdf5'
model3_dir = G.MOD + 'deep_nodense_padded/temp_model.hdf5'
model4_dir = G.MOD + 'deep_nodense_square/temp_model.hdf5'
folder_dir = G.TST
model1 = load_model(model1_dir)
model2 = load_model(model2_dir)
model3 = load_model(model3_dir)
model4 = load_model(model4_dir)
breed_folders = sorted(os.listdir(folder_dir))
img_pred_list = []
for idx, breed in enumerate(breed_folders):
print breed
img_dir = folder_dir + breed + '/'
img_list = os.listdir(img_dir)
for img_loc in img_list:
img_path = img_dir + img_loc
img = Image.open(img_path)
if img.mode != 'RGB':
continue
img = img.resize((model1.input_shape[3], model1.input_shape[2]))
img = np.asarray(img, dtype='float32')
img /= 255.
img = np.transpose(img, (2,0,1))
img = np.array([img])
try:
class_pred1 = model1.predict(img)
class_pred2 = model2.predict(img)
class_pred3 = model3.predict(img)
class_pred4 = model4.predict(img)
except:
print "Pic didn't work"
continue
class_pred1 = class_pred1.flatten()
class_pred2 = class_pred2.flatten()
class_pred3 = class_pred3.flatten()
class_pred4 = class_pred4.flatten()
class_pred = class_pred1 + class_pred2 + class_pred3 + class_pred4
sort_pred = np.argsort(class_pred)[:-6:-1]
if idx == sort_pred[0]:
ens1_1 = True
else:
ens1_1 = False
if idx in sort_pred:
ens5_1 = True
else:
ens5_1 = False
img_pred_list.append([ens1_1, ens5_1])
results_df = pd.DataFrame(img_pred_list)
return results_df.mean()
def test_model_saving_to_pre_created_h5py_file():
model, x = _get_sample_model_and_input()
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
with h5py.File(fname, mode='r+') as h5file:
save_model(model, h5file)
loaded_model = load_model(h5file)
out2 = loaded_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test non-default options in h5
with h5py.File('does not matter', driver='core',
backing_store=False) as h5file:
save_model(model, h5file)
loaded_model = load_model(h5file)
out2 = loaded_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
with h5py.File(fname, mode='r+') as h5file:
g = h5file.create_group('model')
save_model(model, g)
loaded_model = load_model(g)
out2 = loaded_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
def test_functional_model_saving():
inputs = Input(shape=(3,))
x = Dense(2)(inputs)
outputs = Dense(3)(x)
model = Model(inputs, outputs)
model.compile(loss=losses.MSE,
optimizer=optimizers.Adam(),
metrics=[metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_saving_overwrite_option_gcs():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
org_weights = model.get_weights()
new_weights = [np.random.random(w.shape) for w in org_weights]
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(
filename='test_saving_overwrite_option_gcs.h5')
# we should not use same filename in several tests to allow for parallel
# execution
save_model(model, gcs_filepath)
model.set_weights(new_weights)
with patch('keras.engine.saving.ask_to_proceed_with_overwrite') as ask:
ask.return_value = False
save_model(model, gcs_filepath, overwrite=False)
ask.assert_called_once()
new_model = load_model(gcs_filepath)
for w, org_w in zip(new_model.get_weights(), org_weights):
assert_allclose(w, org_w)
ask.return_value = True
save_model(model, gcs_filepath, overwrite=False)
assert ask.call_count == 2
new_model = load_model(gcs_filepath)
for w, new_w in zip(new_model.get_weights(), new_weights):
assert_allclose(w, new_w)
file_io_proxy.delete_file(gcs_filepath) # cleanup
def test_saving_overwrite_option():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
org_weights = model.get_weights()
new_weights = [np.random.random(w.shape) for w in org_weights]
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
model.set_weights(new_weights)
with patch('keras.engine.saving.ask_to_proceed_with_overwrite') as ask:
ask.return_value = False
save_model(model, fname, overwrite=False)
ask.assert_called_once()
new_model = load_model(fname)
for w, org_w in zip(new_model.get_weights(), org_weights):
assert_allclose(w, org_w)
ask.return_value = True
save_model(model, fname, overwrite=False)
assert ask.call_count == 2
new_model = load_model(fname)
for w, new_w in zip(new_model.get_weights(), new_weights):
assert_allclose(w, new_w)
os.remove(fname)
def cnn_models(): cnn_modi = load_model(os.path.join(way,"models/cnn_modi.h5")) cnn_kejriwal = load_model(os.path.join(way,"models/cnn_kejriwal.h5")) cnn_modi.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) cnn_kejriwal.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) graph = tf.get_default_graph() return cnn_kejriwal, cnn_modi, graph
def test_sequential_model_saving_2():
# test with custom optimizer, loss
custom_opt = optimizers.rmsprop
custom_loss = losses.mse
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(Dense(3))
model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
load_kwargs = {'custom_objects': {'custom_opt': custom_opt,
'custom_loss': custom_loss}}
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname, **load_kwargs)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath, **load_kwargs)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def predict(images):
if len(images) == 4:
size = [120, 120]
model = load_model('test2.h5')
elif len(images) == 16:
size = [60, 60]
model = load_model('test4.h5')
elif len(images) == 25:
size = [60, 60]
model = load_model('test5.h5')
images = np.array(images).astype('float32') / 255
#
# mean = 130
# std = 80
# images = (np.array(images) - mean) / (std + 1e-7)
x = []
for i in range(images.shape[0]):
x.append(cv2.resize(images[i], (size[0], size[1]), interpolation=cv2.INTER_AREA)[np.newaxis, :, :, np.newaxis])
test_preds = model.predict(x)
test_preds = np.argmax(test_preds, axis=2)
labels = list(test_preds)
return labels
def test_model_saving_to_pre_created_h5py_file():
inputs = Input(shape=(3,))
x = Dense(2)(inputs)
outputs = Dense(3)(x)
model = Model(inputs, outputs)
model.compile(loss=losses.MSE,
optimizer=optimizers.Adam(),
metrics=[metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
with h5py.File(fname, mode='r+') as h5file:
save_model(model, h5file)
loaded_model = load_model(h5file)
out2 = loaded_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test non-default options in h5
with h5py.File('does not matter', driver='core',
backing_store=False) as h5file:
save_model(model, h5file)
loaded_model = load_model(h5file)
out2 = loaded_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
def try_load_model(self):
filename = self.model_filename
if os.path.exists(filename):
self.model = load_model(filename, custom_objects=self.model_custom_objects())
self.target_model = load_model(filename, custom_objects=self.model_custom_objects())
return True
return False
def test_export_keras_model(_, _1):
Constant.MAX_ITER_NUM = 1
Constant.MAX_MODEL_NUM = 1
Constant.SEARCH_MAX_ITER = 1
Constant.T_MIN = 0.8
train_x = np.random.rand(100, 25, 25, 1)
train_y = np.random.randint(0, 5, 100)
test_x = np.random.rand(100, 25, 25, 1)
clean_dir(TEST_TEMP_DIR)
clf = ImageClassifier(path=TEST_TEMP_DIR, verbose=False, resume=False)
clf.n_epochs = 100
clf.fit(train_x, train_y)
score = clf.evaluate(train_x, train_y)
assert score <= 1.0
model_file_name = os.path.join(TEST_TEMP_DIR, 'test_keras_model.graph')
clf.export_keras_model(model_file_name)
from keras.models import load_model
model = load_model(model_file_name)
results = model.predict(test_x)
assert len(results) == len(test_x)
del model, results, model_file_name
model_file_name = os.path.join(TEST_TEMP_DIR, 'test_autokeras_model.pkl')
clf.export_autokeras_model(model_file_name)
from autokeras.utils import pickle_from_file
model = pickle_from_file(model_file_name)
results = model.predict(test_x)
assert len(results) == len(test_x)
score = model.evaluate(train_x, train_y)
assert score <= 1.0
before = model.graph
model.fit(train_x, train_y, train_x, train_y)
assert model.graph == before
clean_dir(TEST_TEMP_DIR)
clf = ImageRegressor(path=TEST_TEMP_DIR, verbose=False, resume=False)
clf.n_epochs = 100
clf.fit(train_x, train_y)
score = clf.evaluate(train_x, train_y)
assert score >= 0.0
model_file_name = os.path.join(TEST_TEMP_DIR, 'test_keras_model.graph')
clf.export_keras_model(model_file_name)
from keras.models import load_model
model = load_model(model_file_name)
results = model.predict(test_x)
assert len(results) == len(test_x)
del model, results, model_file_name
model_file_name = os.path.join(TEST_TEMP_DIR, 'test_autokeras_model.pkl')
clf.export_autokeras_model(model_file_name)
from autokeras.utils import pickle_from_file
model = pickle_from_file(model_file_name)
results = model.predict(test_x)
assert len(results) == len(test_x)
score = model.evaluate(train_x, train_y)
assert score >= 0.0
clean_dir(TEST_TEMP_DIR)
def robust_load_model(filepath, custom_objects=None):
try:
model = load_model(filepath, custom_objects=custom_objects)
except ValueError:
import h5py
f = h5py.File(filepath, 'r+')
del f['optimizer_weights']
f.close()
model = load_model(filepath, custom_objects=custom_objects)
def __init__(self,srccode="opencv"):
self.fd=FaceDetection(srccode)
# parameters
emotion_model_path = os.path.join(gnu_code_path,'trained_models/emotion_models/simple_CNN.530-0.65.hdf5')
gender_model_path = os.path.join(gnu_code_path,'trained_models/gender_models/simple_CNN.81-0.96.hdf5')
# loading models
self.emotion_classifier = load_model(emotion_model_path)
self.gender_classifier = load_model(gender_model_path)
def detect(seq,irrWindowSz): irrigationRegionRegressor = load_model(pIrrRegionRegressorCNN) seqPP = preprocessData(seq) irrTimepoints = irrigationRegionRegressor.predict(seqPP) seqIrr = cropSequenceWRTIrrigations(seq, irrTimepoints, irrWindowSz) irrigationDetector = load_model(pIrrDetectorCNN) seqIrr2 = preprocessDataForIrrDetector(seqIrr) seqIrr2 = seqIrr2[..., np.newaxis] irrMask = irrigationDetector.predict(seqIrr2) return irrTimepoints, seqIrr, irrMask
def test_saving_unused_layers_is_ok():
a = Input(shape=(256, 512, 6))
b = Input(shape=(256, 512, 1))
c = Lambda(lambda x: x[:, :, :, :1])(a)
model = Model(inputs=[a, b], outputs=c)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
load_model(fname)
os.remove(fname)
def load_model(self, epoch=None):
'''
Loads a saved model. If epoch id is provided, will load the corresponding model. Or else,
will load the best model.
'''
if not epoch:
self.model = load_model("%s.model" % self.model_name_prefix,
custom_objects=self.custom_objects)
else:
self.model = load_model("%s_%d.model" % (self.model_name_prefix, epoch),
custom_objects=self.custom_objects)
self.model.summary()
self.data_processor = pickle.load(open("%s.dataproc" % self.model_name_prefix, "rb"))
def __init__(self):
print("Load registry")
self.registry = pickle.load(open('../models/facerecog/registry.pkl','rb'))
self.class_labels = pickle.load(open('../models/facerecog/class_labels.pkl','rb'))
print("Load Facerecog model")
self.model = load_model('../models/facerecog/final_classifier.h5')
print("Extract Siamese model")
full_model = load_model('../models/facerecog/facerecog_2.h5',custom_objects={'triplet_loss': triplet_loss})
print("Load inception model")
self.frmodel = full_model.get_layer('model_1')
self.image_w = 96
self.image_h = 96
self.detector = dlib.get_frontal_face_detector()
self.align_dlib = AlignDlib()
def test_sequential_model_saving():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(RepeatVector(3))
model.add(TimeDistributed(Dense(3)))
model.compile(loss=losses.MSE,
optimizer=optimizers.RMSprop(lr=0.0001),
metrics=[metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model = load_model(fname)
os.remove(fname)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
out = model.predict(x)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
def classify(self, examples, output, model=None, finishBeforeReturn=False, replaceRemoteFiles=True):
print >> sys.stderr, "Predicting devel examples"
output = os.path.abspath(output)
# Return a new classifier instance for following the training process and using the model
classifier = copy.copy(self)
if model == None:
classifier.model = model = self.model
model = os.path.abspath(model)
model = self.connection.upload(model, uncompress=True, replace=replaceRemoteFiles)
classifier.predictions = self.connection.getRemotePath(output, True)
examples = self.getExampleFile(examples, replaceRemote=replaceRemoteFiles)
classifier._filesToRelease = [examples]
self.kerasModel = load_model(model)
numFeatures = self.kerasModel.layers[0].get_input_shape_at(0)[1]
features, classes = datasets.load_svmlight_file(examples, numFeatures)
#features = features.toarray()
#predictions = self.kerasModel.predict(features, 128, 1)
predictions = self.kerasModel.predict_generator(predict_batch_generator(features, 1), features.shape[0] / 1)
predClasses = predictions.argmax(axis=-1)
predictionsPath = self.connection.getRemotePath(output, False)
with open(predictionsPath, "wt") as f:
for i in range(predictions.shape[0]):
f.write(str(predClasses[i] + 1) + " " + " ".join([str(x) for x in predictions[i]]) + "\n")
def main():
"""Generate different test models and save them to the given directory."""
if len(sys.argv) != 3:
print('usage: [model name] [destination file path]')
sys.exit(1)
else:
model_name = sys.argv[1]
dest_path = sys.argv[2]
get_model_functions = {
'small': get_test_model_small,
'sequential': get_test_model_sequential,
'full': get_test_model_full
}
if not model_name in get_model_functions:
print('unknown model name: ', model_name)
sys.exit(2)
assert K.backend() == "tensorflow"
assert K.floatx() == "float32"
assert K.image_data_format() == 'channels_last'
np.random.seed(0)
model_func = get_model_functions[model_name]
model = model_func()
model.save(dest_path, include_optimizer=False)
# Make sure models can be loaded again,
# see https://github.com/fchollet/keras/issues/7682
model = load_model(dest_path)
print(model.summary())
def test_saving_multiple_metrics_outputs():
inputs = Input(shape=(5,))
x = Dense(5)(inputs)
output1 = Dense(1, name='output1')(x)
output2 = Dense(1, name='output2')(x)
model = Model(inputs=inputs, outputs=[output1, output2])
metrics = {'output1': ['mse', 'binary_accuracy'],
'output2': ['mse', 'binary_accuracy']
}
loss = {'output1': 'mse', 'output2': 'mse'}
model.compile(loss=loss, optimizer='sgd', metrics=metrics)
# assure that model is working
x = np.array([[1, 1, 1, 1, 1]])
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
model = load_model(fname)
os.remove(fname)
out2 = model.predict(x)
assert_allclose(out, out2, atol=1e-05)
def test_sequential_model_saving_2():
# test with funkier config
model = Sequential()
model.add(Dense(2, input_dim=3))
model.add(RepeatVector(3))
model.add(TimeDistributed(Dense(3)))
model.compile(loss=objectives.MSE,
optimizer=optimizers.RMSprop(lr=0.0001),
metrics=[metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
out = model.predict(x)
fname = 'tmp_' + str(np.random.randint(10000)) + '.h5'
save_model(model, fname)
new_model = load_model(fname)
os.remove(fname)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
out = model.predict(x)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
def testModel():
windowSz = 300
model = load_model('dpa_beta_0.38.h5')
pTrainingData = "trainDoubleExpHeating_60Hz.h5"
print("loading synthetic data " + pTrainingData)
with h5py.File(pTrainingData, 'r') as hf:
x_test = np.array(hf.get('x_test'))
y_test = np.array(hf.get('y_test'))
x_test = x_test[..., np.newaxis]
y_ai = model.predict(x_test)
print('ai' + str(y_ai[0,:]) + '\ngt' + str(y_test[0,:]) + '\n')
print('ai' + str(y_ai[1,:]) + '\ngt' + str(y_test[1,:]) + '\n')
print('ai' + str(y_ai[2,:]) + '\ngt' + str(y_test[2,:]) + '\n')
print("loading io. data ")
pTrainingData="../Data/irr_dr_training_new.mat"
f = h5py.File(pTrainingData, "r")
bg = f["seqBackground"]
fg = f["seqSpuelung"]
x_fg = ThermographicDataHandler.slidingWindowPartitioning(np.array(fg.value).T, windowSz, 150)
x_bg = ThermographicDataHandler.slidingWindowPartitioning(np.array(bg.value).T, windowSz, 150)
# x_ai = model.predict(encoder.predict(scaleData(x_fg[:,0,:])))
# x_ai_bg = model.predict(encoder.predict(scaleData(x_bg[:,0,:])))
x_fg = x_fg[..., np.newaxis]
x_bg = x_bg[..., np.newaxis]
x_ai = model.predict((scaleData(x_fg[:,0,:,:])))
x_ai_bg = model.predict((scaleData(x_bg[:,0,:,:])))
print("fg: " + str(x_ai[0,]))
print("bg_1: " + str(x_ai_bg[0,]))
print("bg_2: " + str(x_ai_bg[1,]))
def autoEncoderGen(path, input_shape=theShape):
if os.path.exists(path):
print('loading: ' + str(sorted(os.listdir('models/'))[-1]))
autoencoder = load_model(os.path.join('models', sorted(os.listdir('models/'))[-1]))
name = str(sorted(os.listdir('models/'))[-1])
print('loaded: ' + name)
else:
print('No previous model found.')
print('Building a new model.')
input_img = Input(shape=input_shape) # adapt this if using `channels_first` image data format
x = UpSampling2D((2, 2))(input_img)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2,2))(x)
x = Conv2D(8, (5, 5), activation='relu', padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = x
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = Conv2D(8, (5, 5), activation='relu', padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
decoded = Conv2D(3, (3, 3), activation='relu', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.save('models/autoencoder.h5')
name = 'autoencoderV.h5'
eye_temp = name.replace('.h5', '')
try:
eye = int(eye_temp.replace('autoencoderV', '')) - 10000000
except ValueError:
eye = 0
return autoencoder, name, eye
def load_params():
X_test = os.listdir('./test-jpg')
X_test = [fn.replace('.jpg', '') for fn in X_test]
model = load_model('model_amazon6.h5', custom_objects={'fbeta': train_keras.fbeta})
with open('tag_columns.txt', 'r') as f:
tag_columns = f.read().split('\n')
return X_test, model, tag_columns
def load(self, file, compile=False):
try:
del self.network
except Exception:
pass
self.network = load_model(file, custom_objects={"objective_function_for_policy":AIPlayer.objective_function_for_policy,
"objective_function_for_value":AIPlayer.objective_function_for_value}, compile=compile)
def test_sequential_model_saving():
model = Sequential()
model.add(Dense(2, input_dim=3))
model.add(Dense(3))
model.compile(loss='mse', optimizer='rmsprop', metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
fname = 'tmp_' + str(np.random.randint(10000)) + '.h5'
save_model(model, fname)
new_model = load_model(fname)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
out = model.predict(x)
out2 = new_model.predict(x)
assert_allclose(out, out2, atol=1e-05)
# test load_weights on model file
model.load_weights(fname)
os.remove(fname)
def load(path, opts, vars):
try:
print('\nLoading model\nCreating session and graph')
server = tf.train.Server.create_local_server()
sess = tf.Session(server.target)
graph = tf.get_default_graph()
backend.set_session(sess)
model_path = path + '.' + opts['network'] + '.h5'
print('Loading model from {}'.format(model_path))
model = load_model(model_path);
print('Create prediction function')
model._make_predict_function()
with graph.as_default():
with sess.as_default():
input_shape = list(model.layers[0].input_shape)
input_shape[0] = 1
model.predict(np.zeros(tuple(input_shape)))
vars['graph'] = graph
vars['session'] = sess
vars['model'] = model
except Exception as e:
print_exception(e, 'load')
sys.exit()
def __init__(self, nb_classes, model, seq_length,
saved_model=None, features_length=2048):
"""
`model` = one of:
lstm
lrcn
mlp
conv_3d
c3d
`nb_classes` = the number of classes to predict
`seq_length` = the length of our video sequences
`saved_model` = the path to a saved Keras model to load
"""
# Set defaults.
self.seq_length = seq_length
self.load_model = load_model
self.saved_model = saved_model
self.nb_classes = nb_classes
self.feature_queue = deque()
# Set the metrics. Only use top k if there's a need.
metrics = ['accuracy']
if self.nb_classes >= 10:
metrics.append('top_k_categorical_accuracy')
# Get the appropriate model.
if self.saved_model is not None:
print("Loading model %s" % self.saved_model)
self.model = load_model(self.saved_model)
elif model == 'lstm':
print("Loading LSTM model.")
self.input_shape = (seq_length, features_length)
self.model = self.lstm()
elif model == 'lrcn':
print("Loading CNN-LSTM model.")
self.input_shape = (seq_length, 80, 80, 3)
self.model = self.lrcn()
elif model == 'mlp':
print("Loading simple MLP.")
self.input_shape = (seq_length, features_length)
self.model = self.mlp()
elif model == 'conv_3d':
print("Loading Conv3D")
self.input_shape = (seq_length, 80, 80, 3)
self.model = self.conv_3d()
elif model == 'c3d':
print("Loading C3D")
self.input_shape = (seq_length, 80, 80, 3)
self.model = self.c3d()
else:
print("Unknown network.")
sys.exit()
# Now compile the network.
optimizer = Adam(lr=1e-5, decay=1e-6)
self.model.compile(loss='categorical_crossentropy', optimizer=optimizer,
metrics=metrics)
print(self.model.summary())
vel = vel.astype('float32')
feq = np.asarray([feq]*Re.shape[0])
fnet = np.append(feq,np.zeros((feq.shape[0],1,feq.shape[-2],feq.shape[-1])),axis=1)
Re_test = Re_test/Re_max; Re_test = Re_test.astype('float32')
fnet_test = np.empty((1,fnet.shape[1],fnet.shape[-2],fnet.shape[-1]))
vel_test = np.empty((1,vel.shape[1],vel.shape[-2],vel.shape[-1]))
fnet_test[0,:,:,:] = fnet[0,:,:,:]
fnet_test[0,-1,:,:] = Re_test
for i in np.arange(feq.shape[0]):
fnet[i,-1,:,:] = Re[i]
modelx = load_model('cnn2_x.h5')
print('MODELX SUMMARY: ')
print(modelx.summary())
print('------------------------------------------------------')
print('MODELY SUMMARY: ')
modely = load_model('cnn2_y.h5')
print(modely.summary())
velx = modelx.predict(fnet_test)
vely = modely.predict(fnet_test)
print(velx.shape)
print(vely.shape)
print(vel_test.shape)
import tensorflow
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, load_model
from os import walk
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
model_path = './model/image_classifier_model.h5'
model_weights_path = './model/image_classifier_model_weights.h5'
model = load_model(model_path)
model.load_weights(model_weights_path)
test_path = 'Dataset/test_set'
training_path = 'Dataset/training_set'
labels = list()
for (dirpath, dirnames, filenames) in walk(training_path):
for dirname in dirnames:
labels.append(dirname)
labels = sorted(labels)
print(labels)
picture_array = list()
for (dirpath, dirnames, filenames) in walk(test_path):
for filename in filenames:
picture_array.append(test_path + "/" + filename)
break
conversation_name = args.video.split('/')[-1].split('.')[0]
out_file = args.out_dir + conversation_name
if os.path.isfile(out_file + ".pkl") and pd.read_pickle(
out_file +
".pkl").shape[0] > 0: # and os.path.isfile (out_file + ".png"):
print("Video already processed")
exit(1)
# hyper-parameters for bounding boxes shape
emotion_offsets = (20, 40)
# loading models
#face_detector = dlib.get_frontal_face_detector()
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
# starting video streaming
video_capture = cv2.VideoCapture(args.video)
# Frames frequence : fps frame per seconde
fps = video_capture.get(cv2.CAP_PROP_FPS)
length = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
emotions_states = {
from keras.backend import clear_session
import numpy as np
from keras.models import load_model
import tensorflow as tf
clear_session()
np.set_printoptions(suppress=True)
input_graph_name = "dogbreed.h5"
output_graph_name = input_graph_name[:-3] + '.tflite'
model = load_model(input_graph_name)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.post_training_quantize = True
tflite_model = converter.convert()
open(output_graph_name, "wb").write(tflite_model)
print ("generate:",output_graph_name)
def attentions_layer(x):
from keras import backend as K
x1 = x[:,:,1:]
x2 = x[:,:,0:1]
x2 = K.softmax(x2)
# x2 = keras.backend.print_tensor(x2, str(x2))
# x1 = keras.backend.print_tensor(x1, str(x1))
x=x1*x2
# x = keras.backend.print_tensor(x, str(x))
return x
hidden_size = args.hidden_size
if args.pretrained_name is not None:
from keras.models import load_model
model = load_model(args.pretrained_name)
print("loaded model",model.layers[0].input_shape[1])
# ml = model.layers[0].input_shape[1]
# if (ml != max_length):
# print("model length",ml,"different from data length",max_length)
# max_length = ml
else:
# model = Sequential()
# model.add(Embedding(len(vocab), len(vocab), embeddings_initializer='identity', trainable=False, input_shape=(max_length,)))
# model.add(LSTM_use(hidden_size, return_sequences=True))
# model.add(LSTM_use(max_output + 1, return_sequences=False))
# model.add(Dense(max_output +1))
# model.add(Activation('softmax'))
inputs = Input(shape=(None,None))
print("k",inputs.shape)
callbacks=callbacks_list,
verbose=1)
# --------------------------Logs of loss and acc---------------------------------
train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(epochs)
max_acc_val = max(val_acc)
min_loss_val =min(val_loss)
print('best_acc',max(val_acc), 'min loss', min_loss_val)
#-------------------inference zone
#Delete existing last model
del model
model = load_model(filename+'_'+h5_name)
print ('Evaluation Best Model')
test_loss, test_acc=model.evaluate(x=x_test, y=y_test, batch_size=batch_size, verbose=1, sample_weight=None, steps=None)
print('Test_acc',test_acc)
#------------report result CSV
row_n=[str(job_id),script_name,str(round(max_acc_val,5)),str(round(min_loss_val,5)), str(round(test_acc,5)),str(round(test_loss,5)),str(lr),str(s),str(mw),str(multi),str(sig),h5_name,time.strftime("%h/%d/%m/%Y")]
with open(dest_path+'cifar_monogenic_haze'+'.csv', 'a') as f:
writer = csv.writer(f, delimiter='\t',lineterminator='\n')
writer.writerow(row_n)
f.close()
end_time = time.monotonic()
#end_time = time.time()
print(end_time)
from keras.preprocessing import image
from PIL import Image
# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
# Define a flask app
app = Flask(__name__)
# Model saved with Keras model.save()
MODEL_PATH = 'model/malariaModel.h5'
# Load your trained model
model = load_model(MODEL_PATH)
# model._make_predict_function() # Necessary
print('Model loaded. Start serving...')
print('Model loaded. Check http://127.0.0.1:5000/')
def model_predict(img_path, model):
img = image.load_img(img_path, target_size=(130, 130))
# Preprocessing the image
x = image.img_to_array(img)
# x = np.true_divide(x, 255)
x = np.expand_dims(x, axis=0)
# Be careful how your trained model deals with the input
# Saving evolution history of epochs in this fold
f = open("%s/history_fold_%d.txt" % (directory, fold), 'w')
f.write("best_epoch: %d\n" % best_epoch)
f.write(
"epoch,training_accuracy,training_loss,validation_accuracy,validation_loss\n"
)
for i in range(MAX_EPOCHS):
f.write("%d,%f,%f,%f,%f\n" %
(i, epoch_history['acc'][i], epoch_history['loss'][i],
epoch_history['val_acc'][i], epoch_history['val_loss'][i]))
f.close()
# load the best model saved on disk
del model
model = load_model("%s/model_fold_%d.h5" % (directory, fold))
evaluation = model.evaluate(x=x_test, y=y_test)
logging.info("Accuracy: %f" % evaluation[1])
prediction = model.predict(x_test)
# save predictions to disk
test_indexes = test_indexes.reshape(test_indexes.shape[0], 1)
tweet_ids = data_set[:, DATA_SET_USER_ID][test_indexes]
true_labels = np.asarray(y_corpus_raw, dtype=int)[test_indexes]
class_1 = prediction[:, 2]
class_2 = prediction[:, 1]
class_3 = prediction[:, 0]
output = np.append(tweet_ids, true_labels, axis=1)
output = np.append(output,
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.utils import np_utils
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten,Dropout
from keras.optimizers import Adam
import cv2
model = Sequential()
model = load_model('my_model.h5')
X_test = []
y_test = []
fp = open('little/test.txt', "r")
line = fp.readline()
while line:
lab = np.zeros(15,dtype = np.float32)
mid = line.find(".jpg")+4
lab[int(line[mid:len(line)])] = 1
img = cv2.imread(line[0:mid])
img = cv2.resize(img,dsize=(160,120))
X_test.append(img)
y_test.append(lab)
line = fp.readline()
X_test = np.asarray(X_test,dtype=np.float32)
y_test = np.asarray(y_test)
X_test = X_test.reshape(-1, 3,160, 120)/255.
y_test = y_test.reshape(-1, 15)
def load_model(self):
self.online_model = load_model(self.name)
filelist = []
labels = []
for file in listdir('./encoded/NORMAL'):
filelist.append('./encoded/NORMAL/{}'.format(file))
labels.append(0)
for file in listdir('./encoded/PNEUMONIA'):
filelist.append('./encoded/PNEUMONIA/{}'.format(file))
labels.append(1)
generator = DataGenerator(filelist, labels)
model = load_model('./model.h5',
custom_objects={
'myDense': myDense,
'myConv2d': myConv2d,
'maxpool': maxpool
})
probs = model.evaluate_generator(generator)
print("The model had an accuracy of {}% with a loss of {}.".format(
probs[1] * 100, probs[0]))
yhat_probs = model.predict_generator(generator, verbose=0)
yhat_probs = yhat_probs[:, 0]
yhat_probs = yhat_probs.round().astype(int)
cm = ConfusionMatrix(labels, yhat_probs)
LEARNING_RATE = 0.001
eps = float(sys.argv[1])
directions = [Direction.North, Direction.South, Direction.East, Direction.West, Direction.Still]
actions = np.array([0,1,2,3,4])
ecart_type = 12
reward_gaussian_weight = np.zeros(128)
for i in range(128):
reward_gaussian_weight[i] = 3*exp(-i**2/(2*ecart_type**2))
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.logging.set_verbosity(tf.logging.ERROR)
if loadmodel:
model = load_model('my_model.h5', custom_objects={'huber_loss': huber_loss, 'tf': tf})
else:
model = create_model(443, 5, LEARNING_RATE, 'my_model.h5')
secondDropoff = False
total_halite = 0
total_reward = 0
counter = 0
hash_dim_turn = {32:400, 40:425, 48:450, 56:475, 64:500}
np.random.seed()
# This game object contains the initial game state.
game = hlt.Game()
game.ready("DQN-PER-Para3-GoodMove")
""" <<<Game Loop>>> """
while True:
def load(self, path):
self.model = m.load_model(path)
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.models import load_model
import random as r
import pickle as p
import numpy as np
from PIL import Image
model = load_model('Spiral_Model-84.38-1020053228.h5')
data = p.load(open("train_model/picarray.pickle", "rb"))
repeats = int(input("How many tests? "))
randpics=[]
for repeat in range(1,repeats+1):
randpics.append(data[r.randint(0, len(data)-1)])
predictions = model.predict(np.array([pic[0] for pic in randpics]))
#randpics = [[[value*255 for value in row] for row in randpic[0]] for randpic in randpics]]
guesses=[]
guesscorrect = 0
for i in range(len(randpics)):
print ("\nTEST",i+1,"/",repeats)
img = Image.fromarray(np.uint8(randpics[i][0][0]*255), mode="L")#(randpics[0][0])*255)
img.show()
guess = True if int(input("Parkinsons? ")) else False
empty_ids_df = pd.DataFrame(most_senti,
index=phrase_id_list,
columns=["Sentiment"])
submission_df = submission_df.append(empty_ids_df)
submission_df.index.name = "PhraseId"
submission_df.sort_index(inplace=True)
# submission_df.to_csv("../data/output/submissions/lstm_submission_matrix_fill.csv")
submission_df.to_csv(
"../data/output/submissions/sk_rf_submission_matrix_fill.csv")
if __name__ == "__main__":
# For reproducibility
np.random.seed(2)
tf.set_random_seed(2)
# X_train, X_val, X_test, X_test_id, y_train, y_val = gen_train_val_test_data() # vector
X_train, X_val, X_test, y_train, y_val = gen_train_val_test_matrix(
) # matrix
print(
f"X_train.shape:{X_train.shape}\nX_val.shape:{X_val.shape}\nX_test.shape:{X_test.shape}\ny_train.shape:{y_train.shape}\ny_val.shape:{y_val.shape}\n"
)
"""
model_train_val(X_train, X_val, y_train, y_val)
# plot_hist()
"""
model_name = "best_model_05_0.05.hdf5"
model = load_model(f"../data/output/models/{model_name}")
model_predict(model, X_test, X_val, y_val)
self.xai_rnn.real_sp,
1]
return test_data, P_test_1, P_test_2
parser = argparse.ArgumentParser(usage="it's usage tip.",
description="help info.")
parser.add_argument("--f",
type=int,
default=5,
required=True,
help="the select feature number.")
args = parser.parse_args()
print '[Load model...]'
model = load_model('model/O1_Bi_Rnn.h5')
PATH_TEST_DATA = 'data/elf_x86_32_gcc_O1_test.pkl'
n_fea_select = args.f
#PATH_TEST_DATA = 'elf_x86_32_gcc_O0_test.pkl'
print '[Load data...]'
data = pickle.load(file(PATH_TEST_DATA))
print "len(data):", len(data)
data_num = len(data[0])
print 'Data_num:', data_num
seq_len = len(data[0][0])
print 'Sequence length:', seq_len
### Padding sequence ....
x_test = pad_sequences(data[0],
maxlen=seq_len,
dtype='int32',
plt.legend()
plt.show()
#GRÁFICO DE PERDA DE VALIDAÇÃO E TREINO
plt.plot(epochs, loss, 'b', label='Treino')
plt.plot(epochs, val_loss, 'r', label='Validação')
plt.title('Perda por época')
plt.xlabel('épocas')
plt.ylabel('porcentagem')
plt.grid(True)
plt.legend()
plt.show()
#CARREGA MODELO TREINADO
model = load_model('modelo.h5')
#MOSTRA COMO A REDE ESTÁ CONFIGURADA
sumario_modelo = model.summary()
#DIRETÓRIO DAS IMAGENS QUE DESEJA USAR COMO TESTE
imagens_teste = [
'C:\\Users\\Brennus\\Downloads\\Dataset_Teste\\{}'.format(i)
for i in os.listdir(diretorio_teste)
]
#NÚMERO DE IMAGENS PRESENTE DENTRO DE imagens_teste
ImagensParaAvaliar = 40
#NÚMERO DE COLUNAS NA PLOTAGEM DAS IMAGENS DE TESTE
coluna_imagens_teste = 5
import nltk, pickle, json, random
from nltk.stem import WordNetLemmatizer
from keras.models import load_model
import numpy as np
model = load_model('chatbotmodel.h5')
lemmatizer = WordNetLemmatizer()
intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))
def clean_up_sentence(sentence):
# tokenize the pattern - split words into array
sentence_words = nltk.word_tokenize(sentence)
# stem each word - create short form for word
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
#IMPORT LIBRARIES
import numpy as np
import cv2
from keras.models import load_model
import imutils
from imutils.contours import sort_contours
model = load_model('./model_jd_96.h5')
def prep_labels():
labelNames = "0123456789"
labelNames += "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
#labelNames += "abcdefghijklmnopqrstuvwxyz"
labelNames = [l for l in labelNames]
return labelNames
def get_image_predict(image_path):
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# perform edge detection, find contours in the edge map, and sort the
# resulting contours from left-to-right
edged = cv2.Canny(blurred, 30, 150)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sort_contours(cnts, method="left-to-right")[0]
# initialize the list of contour bounding boxes and associated
# characters that we'll be OCR'ing
keras_model = best_dnn.model_
history = keras_model.fit(X_train_ori,
y_train_ori,
epochs=all_epochs,
batch_size=all_batches,
verbose=0)
keras_model.save(model_name)
with open(model_name + ".pickle", 'wb') as file_pi:
pickle.dump(history.history, file_pi)
# Delete the existing model.
del keras_model
# TESTING PHASE
X_test = np.load(X_test_name)
y_test = np.load(y_test_name)
model = load_model(model_name)
y_pred = 1 * (model.predict(X_test) > 0.5)
y_pred = y_pred.flatten()
conf_matrix = confusion_matrix(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
print("\n Accuracy on test set: " + str(accuracy))
print(conf_matrix)
# Testing on training to check learning
y_pred = 1 * (model.predict(X_train) > 0.5)
y_pred = y_pred.flatten()
conf_matrix = confusion_matrix(y_train, y_pred)
accuracy = accuracy_score(y_train, y_pred)
print("\nAccuracy on train set: " + str(accuracy))
print(conf_matrix)
NUM_CLASSES = test_segs.shape[1] + 1 # update for required number of classes
# boundary names should be a list of strings with length = NUM_CLASSES - 1
# class names should be a list of strings with length = NUM_CLASSES
AREA_NAMES = ["area_" + str(i) for i in range(NUM_CLASSES)]
BOUNDARY_NAMES = ["boundary_" + str(i) for i in range(NUM_CLASSES - 1)]
PATCH_CLASS_NAMES = ["BG"]
for i in range(len(BOUNDARY_NAMES)):
PATCH_CLASS_NAMES.append(BOUNDARY_NAMES[i])
GSGRAD = 1
CUSTOM_OBJECTS = dict(list(custom_losses.custom_loss_objects.items()) +
list(custom_metrics.custom_metric_objects.items()))
eval_imdb = imdb.ImageDatabase(images=test_images, labels=None, patch_labels=test_patch_labels, segs=test_segs, image_names=test_image_names,
boundary_names=BOUNDARY_NAMES, area_names=AREA_NAMES,
fullsize_class_names=AREA_NAMES, patch_class_names=PATCH_CLASS_NAMES, num_classes=NUM_CLASSES, name=TEST_DATA_NAME, filename=TEST_DATA_NAME, mode_type='fullsize')
batch_size = 992 # CURRENTLY THIS NEEDS TO BE CHOSEN AS A VALUE WHICH IS A FACTOR OF THE AREA (IN PIXELS) OF THE FULL IMAGE (i.e. 992 is a factor of a 761856 (1536x496) pixel image [992 x 768 = 761856])
network_folder = parameters.RESULTS_LOCATION + "\\2021-04-21 14_35_20 Complex CNN 32x32 myexampledata\\" # name of network folder for which to evaluate model
model_name = "model_epoch06.hdf5" # name of model file inside network folder to evaluate
loaded_model = load_model(network_folder + "/" + model_name, custom_objects=CUSTOM_OBJECTS)
aug_fn_arg = (aug.no_aug, {})
eval_helper.evaluate_network(eval_imdb, model_name, network_folder,
batch_size, save_parameters.SaveParameters(pngimages=True, raw_image=True, temp_extra=True, boundary_maps=True, area_maps=True, comb_area_maps=True, seg_plot=True),
gsgrad=GSGRAD, aug_fn_arg=aug_fn_arg, eval_mode='both', boundaries=True, boundary_errors=True, dice_errors=False, col_error_range=None, normalise_input=True, transpose=False)
print (test_image.shape)
else:
test_image= np.expand_dims(test_image, axis=0)
print (test_image.shape)
#%%
print((model.predict(test_image)))
print(model.predict_classes(test_image))
#%%
#saving
from keras.models import model_from_json
from keras.models import load_model
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("model.h5")
print("Saved model to disk")
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
model.save('model.hdf5')
loaded_model=load_model('model.hdf5')
def Single_main(Base_dir,
Docx,
DocY,
epoch=3000,
batch_size=50,
TF=False,
mode=None):
in_shape = (None, None, 1)
# Four_InputX=Docx['4by4_data']
# Four_InputY=DocY['4by4_data']
Five_InputX = Docx['5by5_data']
Five_InputY = DocY['5by5_data']
# Six_InputX=Docx['6by6_data']
# Six_InputY=DocY['6by6_data']
if TF == False:
print("here")
Network = Sequential()
print("here1")
ModelBuild(Network, in_shape)
print("here11")
configure(Network)
else:
H5_file = Base_dir + '/predict_h5file/5by5_ConcatNet.h5'
Network = load_model(H5_file)
timer = ElapsedTimer()
history = LossHistory()
print('/*******************************************************/\n')
print(' Now we begin to train this model.\n')
print('/*******************************************************/\n')
if mode == '4by4':
Network.fit(Four_InputX,
Four_InputY,
epochs=epoch,
batch_size=batch_size,
validation_split=0.1,
shuffle=True)
elif mode == '5by5':
Network.fit(Five_InputX,
Five_InputY,
epochs=epoch,
batch_size=batch_size,
validation_split=0.1,
shuffle=True,
callbacks=[history])
elif mode == '6by6':
Network.fit(Six_InputX,
Six_InputY,
epochs=epoch,
batch_size=batch_size,
validation_split=0.1,
shuffle=True)
# =============================================================================
print('/*******************************************************/')
print(' finished!! ')
timer.elapsed_time()
print('/*******************************************************/\n')
return Network, history
def remove_noisy(x_test_noisy):
autoencoder = load_model('autoencoder.h5')
decoded_imgs = autoencoder.predict(x_test_noisy)
return decoded_imgs
def main(Base_dir, Docx, DocY, epoch=3000, batch_size=50, TF=False):
in_shape = (None, None, 1)
History_4by4 = []
History_5by5 = []
History_6by6 = []
# Iteration_num=int(len(Docx['6by6_data'])/batch_size);
temp_len = max(len(Docx['4by4_data']), len(Docx['5by5_data']),
len(Docx['6by6_data']))
Iteration_num = int(temp_len // batch_size)
Four_num = int(len(Docx['4by4_data']) // batch_size)
Five_num = int(len(Docx['5by5_data']) // batch_size)
Six_num = int(len(Docx['6by6_data']) // batch_size)
Four_InputX = Docx['4by4_data']
Four_InputY = DocY['4by4_data']
Five_InputX = Docx['5by5_data']
Five_InputY = DocY['5by5_data']
Six_InputX = Docx['6by6_data']
Six_InputY = DocY['6by6_data']
###########################################################################################################
if TF == False:
Network = Sequential()
ModelBuild(Network, in_shape)
configure(Network)
else:
H5_file = Base_dir + '/predict_h5file/5by5-1_CNN.h5'
Network = load_model(H5_file)
###########################################################################################################
timer = ElapsedTimer()
print('/*******************************************************/\n')
print(' Now we begin to train this model.\n')
print('/*******************************************************/\n')
###########################################################################################################
for i in range(epoch):
for j in range(Iteration_num):
if (j + 1) * batch_size > len(Docx['4by4_data']):
j0 = j % Four_num
History_4by4.append(
Network.train_on_batch(
Four_InputX[j0 * batch_size:(j0 + 1) *
batch_size, :, :, :],
Four_InputY[j0 * batch_size:(j0 + 1) * batch_size, :]))
else:
History_4by4.append(
Network.train_on_batch(
Four_InputX[j * batch_size:(j + 1) *
batch_size, :, :, :],
Four_InputY[j * batch_size:(j + 1) * batch_size, :]))
if (j + 1) * batch_size > len(Docx['5by5_data']):
j0 = j % Five_num
History_5by5.append(
Network.train_on_batch(
Five_InputX[j0 * batch_size:(j0 + 1) *
batch_size, :, :, :],
Five_InputY[j0 * batch_size:(j0 + 1) * batch_size, :]))
else:
History_5by5.append(
Network.train_on_batch(
Five_InputX[j * batch_size:(j + 1) *
batch_size, :, :, :],
Five_InputY[j * batch_size:(j + 1) * batch_size, :]))
if (j + 1) * batch_size > len(Docx['6by6_data']):
j0 = j % Six_num
History_6by6.append(
Network.train_on_batch(
Six_InputX[j0 * batch_size:(j0 + 1) *
batch_size, :, :, :],
Six_InputY[j0 * batch_size:(j0 + 1) * batch_size, :]))
else:
History_6by6.append(
Network.train_on_batch(
Six_InputX[j * batch_size:(j + 1) *
batch_size, :, :, :],
Six_InputY[j * batch_size:(j + 1) * batch_size, :]))
if (i % 20 == 0):
print('In iteration ' + str(i) +
', The Training detail is : 4by4: ' + str(History_4by4[i]))
print('In iteration ' + str(i) +
', The Training detail is : 5by5: ' + str(History_5by5[i]))
print('In iteration ' + str(i) +
', The Training detail is : 6by6: ' + str(History_6by6[i]) +
'\n')
if TF == True:
h5_dir = Base_dir + '/predict_h5file/total_TF_VCN.h5'
elif TF == False:
h5_dir = Base_dir + '/predict_h5file/total_Non-TF_VCN.h5'
Network.save(h5_dir)
timer.elapsed_time()
print('/*******************************************************/')
print(' finished!! ')
print('/*******************************************************/\n')
return Network
from io import BytesIO
import matplotlib.pyplot as plt
from keras.preprocessing import image
from keras.models import load_model
from keras.applications.inception_v3 import preprocess_input, decode_predictions
import sys
def out_to_file():
file = open("out.txt", 'w')
sys.stdout = file #重定向到文件
# model = load_model(r"C:\Users\zbh11\zhongkong_ft.h5")
out_to_file()
model = load_model(r"C:/Users/江淼/Desktop/2019train/model/zhongkong_ft_2.h5")
def predic(i):
img_path = "C:/Users/江淼/Desktop/2019train/data/test/class" + str(i) + ".jpg"
# img_path = "C:/Users/江淼/Desktop/2019train/data/test/class2.jpg"
# img_path = "F:\\SuperMarket\\SuperMarker2.0\\" + str(i) +".jpg"
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
# print(x)
x = preprocess_input(x)
# print("--------------------preprocessed!----------------")
# print(x)
# print(" ")
preds = model.predict(x)
preds=preds.tolist()
import imutils
import matplotlib.pyplot as plt
from sklearn.preprocessing import Normalizer, LabelEncoder
import time
# Load anh
image = Image.open('test.jpg')
img = cv2.imread('test.jpg')
# Chuyen sang anh mau neu can
image = image.convert('RGB')
# Chuyen du lieu thanh mang
pixels = np.asarray(image)
# Tao bo detector
detector = MTCNN()
# Load FaceNet model
model = load_model('facenet_keras.h5')
# Load model da duoc train
clf = pickle.loads(open('output/clf.pkl', "rb").read())
lb = pickle.loads(open('output/lb.pkl', "rb").read())
# Bo chuan hoa
def normalization(vector):
a = 0
x = []
for i in range(len(vector)):
a = np.sqrt(a**2 + vector[i]**2)
for i in range(len(vector)):
vector[i] = vector[i] / a
return vector
def train_model():
print('Start: Training Model')
root_path = os.getcwd()
train_path = os.path.join(root_path, *TRAINING_SPLIT_DATA_PATH)
test_path = os.path.join(root_path, *TEST_SPLIT_DATA_PATH)
if len(VALIDATION_SPLIT_DATA_PATH) > 0:
val_path = os.path.join(root_path, *VALIDATION_SPLIT_DATA_PATH)
history_name = '{}_ep{}_bs{}.csv'.format(HISTORY_PREFIX, NUM_EPOCHS, BATCH_SIZE)
history_file = os.path.join(root_path, *HISTORY_TRAINING_PATH)
history_file = os.path.join(history_file, history_name)
print('Preparing Data')
# Get Training Data
print('Retrieving Data for Training')
train_features = None
train_label = None
data_retrieved = False
for file_name in os.listdir(train_path):
file_path = os.path.join(train_path, file_name)
if os.path.isfile(file_path):
if DATA_FILE == file_name[:-6]:
data_retrieved = True
np_file = np.load(file_path)
if train_features is None:
train_features = np_file['dataset']
train_label = np_file['labels']
else:
train_features = np.concatenate((train_features, np_file['dataset']), axis=0)
train_label = np.concatenate((train_label, np_file['labels']), axis=0)
if not data_retrieved:
print('Retrieved No Training Data')
if len(VALIDATION_SPLIT_DATA_PATH) == 0:
print('Splitting Training Dataset Into Training and Validation')
x_train, x_val, y_train, y_val = data_manager.train_val_split(train_features, train_label,
val_ratio=VALIDATION_RATIO)
else:
print('Retrieving Data for Validation')
x_train = train_features
y_train = train_label
x_val = None
y_val = None
data_retrieved = False
for file_name in os.listdir(val_path):
file_path = os.path.join(val_path, file_name)
if os.path.isfile(file_path):
if DATA_FILE == file_name[:-6]:
data_retrieved = True
np_file = np.load(file_path)
if x_val is None:
x_val = np_file['dataset']
y_val = np_file['labels']
else:
x_val = np.concatenate((x_val, np_file['dataset']), axis=0)
y_val = np.concatenate((y_val, np_file['labels']), axis=0)
if not data_retrieved:
print('Retrieved No Validation Data')
# Get Testing Dataset
print('Retrieving Data for Testing')
test_features = None
test_label = None
data_retrieved = False
for file_name in os.listdir(test_path):
file_path = os.path.join(test_path, file_name)
if os.path.isfile(file_path):
if DATA_FILE == file_name[:-6]:
data_retrieved = True
np_file = np.load(file_path)
if test_features is None:
test_features = np_file['dataset']
test_label = np_file['labels']
else:
test_features = np.concatenate((test_features, np_file['dataset']), axis=0)
test_label = np.concatenate((test_label, np_file['labels']), axis=0)
if not data_retrieved:
print('Retrieved No Testing Data')
# Use validation as test data
x_test = test_features
y_test = test_label
print('Preparing_Model')
model_path = os.path.join(root_path, *MODELS_PATH)
model_temp_path = os.path.join(model_path, TEMP_MODEL_DIR)
if not os.path.exists(model_temp_path):
os.makedirs(model_temp_path)
if len(PRE_TRAINED_MODEL) == 0:
print('Creating New Model')
model_input = Input(INPUT_SHAPE)
x = inception.build_inception_v4(model_input, enable_reduction=ENABLE_MODEL_REDUCTION)
model = Model(model_input, x, name='inception_v4')
if len(LOAD_WEIGHTS_NEW_MODEL) > 0:
print('Loading Weights From Prior Training Error')
weights2load = os.path.join(root_path, *LOAD_WEIGHTS_NEW_MODEL)
model.load_weights(weights2load)
else:
print('Loading Existing Model')
pre_model_path = os.path.join(root_path, *PRE_TRAINED_MODEL)
cus_obj = None
if LOSS_FUNCTION == 'root_mean_squared_error':
cus_obj = {'root_mean_squared_error': root_mean_squared_error}
model = load_model(pre_model_path, custom_objects=cus_obj)
if LOSS_FUNCTION == 'root_mean_squared_error':
model.compile(loss=root_mean_squared_error, optimizer=LEARNING_OPTIMIZER, metrics=['mae'])
else:
model.compile(loss=LOSS_FUNCTION, optimizer=LEARNING_OPTIMIZER, metrics=['mae'])
print(model.summary())
print('Evaluating Model')
# Write Prediction Log
def log_preds(y, y_, file):
with codecs.open(file, "w", encoding="utf-8") as pred_csv:
pred_csv.write('y_true,y_pred\n')
for l in range(len(y)):
pred_csv.write('{},{}\n'.format(y[l], y_[l]))
# Write Summary Log
log_file = os.path.join(root_path, *SUMMARY_TRAINING_LOG)
if not os.path.exists(log_file):
with codecs.open(log_file, "a", encoding="utf-8") as log_csv:
log_csv.write(
'model_version,i,nb_epochs,batch_size,nb_rotations,mirror_vertical,mirror_horizontal,model_reduction,test_rmse,test_pk\n')
# Create Directory to save all top k models
save_model_path = os.path.join(model_path, 'top_models-test_only_ep({})-bs({})-r({})-rm({})-rd({})'.format(NUM_EPOCHS,
BATCH_SIZE,
NB_ROTATION,
ENABLE_HORIZONTAL_MIRROR or ENABLE_VERTICAL_MIRROR,
ENABLE_MODEL_REDUCTION))
if not os.path.exists(save_model_path):
os.makedirs(save_model_path)
# Create Directory to save all prediction logs
pred_path = os.path.join(root_path, *HISTORY_TRAINING_PATH)
pred_path = os.path.join(pred_path,
'{}_test_only_ep{}_bs{}_r({})_rm({})_mr({})'.format(PREDICTION_LOG, NUM_EPOCHS, BATCH_SIZE,
NB_ROTATION,
ENABLE_HORIZONTAL_MIRROR or ENABLE_VERTICAL_MIRROR,
ENABLE_MODEL_REDUCTION))
if not os.path.exists(pred_path):
os.makedirs(pred_path)
model_path = os.path.join(root_path, *MODELS_PATH)
model_temp_path = os.path.join(model_path, TEMP_MODEL_DIR)
if not os.path.exists(model_temp_path):
os.makedirs(model_temp_path)
# Evaluate Best Validation Models
for file_name in os.listdir(model_temp_path):
file_path = os.path.join(model_temp_path, file_name)
if os.path.isfile(file_path):
if 'rmse' in file_name:
itr = int(file_name[22])
temp_best_val_weights = os.path.join(model_temp_path, 'temp_best_rmse_weights{}.hdf5'.format(itr))
model.load_weights(temp_best_val_weights)
val_preds = model.predict(x_test)
val_preds = val_preds.reshape(val_preds.shape[0])
val_preds = np.nan_to_num(val_preds)
p_k = prediction_probability.predprob(y_test[:, 2], val_preds)
test_rmse_val = sqrt(mean_squared_error(y_test[:, 2], val_preds))
print('Test P_K Score (Val Model {}):'.format(itr))
val_str_pk = '{:.6f}'.format(p_k)
print(val_str_pk)
best_model_name = 'model_rmse__e({})_bs({})_mr({})_i({})_test-rmse({:.6f})_test-pk({}).hdf5'.format(
NUM_EPOCHS,
BATCH_SIZE,
ENABLE_MODEL_REDUCTION,
itr,
test_rmse_val,
val_str_pk)
model.save(os.path.join(save_model_path, best_model_name))
pred_name_file = '{}_val_ep{}_bs{}_mr({})_i({}).csv'.format(PREDICTION_LOG, NUM_EPOCHS, BATCH_SIZE,
ENABLE_MODEL_REDUCTION, itr)
pred_file = os.path.join(pred_path, pred_name_file)
log_preds(y_test[:, 2], val_preds, pred_file)
with codecs.open(log_file, "a", encoding="utf-8") as log_csv:
log_csv.write(
'rmse,{},{},{},{},{},{},{},{:.6f},{}\n'.format(NUM_EPOCHS, itr, BATCH_SIZE,
NB_ROTATION,
ENABLE_VERTICAL_MIRROR,
ENABLE_HORIZONTAL_MIRROR,
ENABLE_MODEL_REDUCTION,
test_rmse_val, val_str_pk))
elif 'avrg' in file_name:
itr = int(file_name[22])
temp_best_avg_weights = os.path.join(model_temp_path,
'temp_best_avrg_weights{}.hdf5'.format(itr))
model.load_weights(temp_best_avg_weights)
avg_preds = model.predict(x_test)
avg_preds = avg_preds.reshape(avg_preds.shape[0])
avg_preds = np.nan_to_num(avg_preds)
p_k = prediction_probability.predprob(y_test[:, 2], avg_preds)
test_rmse_avg = sqrt(mean_squared_error(y_test[:, 2], avg_preds))
print('Test P_K Score (Avg Model {}):'.format(itr))
avg_str_pk = '{:.6f}'.format(p_k)
print(avg_str_pk)
best_model_name = 'model_average_e({})_bs({})_mr({})_i({})_test-rmse({:.6f})_test-pk({}).hdf5'.format(
NUM_EPOCHS,
BATCH_SIZE,
ENABLE_MODEL_REDUCTION,
itr,
test_rmse_avg,
avg_str_pk,)
model.save(os.path.join(save_model_path, best_model_name))
pred_name_file = '{}_avg_ep{}_bs{}_mr({})_i({}).csv'.format(PREDICTION_LOG, NUM_EPOCHS, BATCH_SIZE,
ENABLE_MODEL_REDUCTION, itr)
pred_file = os.path.join(pred_path, pred_name_file)
log_preds(y_test[:, 2], avg_preds, pred_file)
with codecs.open(log_file, "a", encoding="utf-8") as log_csv:
log_csv.write(
'average,{},{},{},{},{},{},{},{:.6f},{}\n'.format(NUM_EPOCHS, itr, BATCH_SIZE,
NB_ROTATION,
ENABLE_VERTICAL_MIRROR,
ENABLE_HORIZONTAL_MIRROR,
ENABLE_MODEL_REDUCTION,
test_rmse_avg, avg_str_pk))
print('Done: Training Model')
smooth = 1.0
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) +
smooth)
img_train = np.load('train_data.npy')
mask_train = np.load('train_mask.npy')
img_test = np.load('test_data.npy')
mask_test = np.load('test_mask.npy')
print("Shape of Image Test")
print(img_test.shape)
model = load_model('model-lv_segmentation2.h5')
model.save_weights('t_lv.h5')
with open('model_architecture.json', 'w') as f:
f.write(model.to_json())
with open('model_architecture.json', 'r') as f:
model_type1 = model_from_json(f.read())
with open('model_architecture.json', 'r') as f:
model_type2 = model_from_json(f.read())
model_type1.load_weights('t_lv.h5')
model_type2.load_weights('t_my.h5')
model_type1.compile(optimizer=Adam(lr=1e-5),
loss='binary_crossentropy',
