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23-07-23
메모리 누수 해결 - 완전한 해결은 아니라 대량의 메모리가 필요 mnist 최적값을 찾는 파티클 개수 찾아야 함
This commit is contained in:
jung-geun
14
README.md
14
README.md
@@ -1,4 +1,5 @@
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[](https://github.com/jung-geun/PSO/actions/workflows/pypi.yml)
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[](https://github.com/jung-geun/PSO/actions/workflows/pypi.yml)
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<a href="https://colab.research.google.com/github/jung-geun/PSO/blob/master/pso2keras.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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# PSO 알고리즘 구현 및 새로운 시도
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# PSO 알고리즘 구현 및 새로운 시도
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@@ -41,13 +42,22 @@ pip install pso2keras==0.1.4
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위의 패키지를 사용하기 위해서는 tensorflow 와 tensorboard 가 설치되어 있어야 합니다
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위의 패키지를 사용하기 위해서는 tensorflow 와 tensorboard 가 설치되어 있어야 합니다
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python 패키지를 사용하기 위한 라이브러리는 아래 코드를 사용합니다
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```python
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from pso import Optimizer
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pso_model = Optimizer(...)
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pso_model.fit(...)
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```
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# 현재 진행 상황
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# 현재 진행 상황
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## 1. PSO 알고리즘 구현
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## 1. PSO 알고리즘 구현
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### 파일 구조
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### 파일 구조
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```plain text
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```plain
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|-- /conda_env # conda 환경 설정 파일
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|-- /conda_env # conda 환경 설정 파일
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| |-- environment.yaml # conda 환경 설정 파일
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| |-- environment.yaml # conda 환경 설정 파일
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|-- /metacode # pso 기본 코드
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|-- /metacode # pso 기본 코드
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@@ -212,7 +222,7 @@ best_score = pso_mnist.fit(
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> 머신러닝 분류 방식에 존재하는 random forest 방식을 이용하여, 오차역전파 함수를 최적화 하는 방법이 있을것 같습니다
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> 머신러닝 분류 방식에 존재하는 random forest 방식을 이용하여, 오차역전파 함수를 최적화 하는 방법이 있을것 같습니다
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>
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>
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> > pso 와 random forest 방식이 매우 유사하다고 생각하여 학습할 때 뿐만 아니라 예측 할 때도 이러한 방식으로 사용할 수 있을 것 같습니다
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> > pso 와 random forest 방식이 매우 유사하다고 생각하여 학습할 때 뿐만 아니라 예측 할 때도 이러한 방식으로 사용할 수 있을 것 같습니다
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>
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>
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> 각
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> 각
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# 참고 자료
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# 참고 자료
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10
mnist.py
10
mnist.py
@@ -106,11 +106,11 @@ loss = [
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pso_mnist = Optimizer(
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pso_mnist = Optimizer(
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model,
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model,
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loss=loss[0],
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loss=loss[0],
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n_particles=100,
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n_particles=1000,
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c0=0.25,
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c0=0.4,
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c1=0.4,
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c1=0.6,
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w_min=0.3,
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w_min=0.5,
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w_max=0.9,
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w_max=0.8,
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negative_swarm=0.1,
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negative_swarm=0.1,
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mutation_swarm=0.2,
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mutation_swarm=0.2,
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particle_min=-5,
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particle_min=-5,
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@@ -1,7 +1,7 @@
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from .optimizer import Optimizer
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from .optimizer import Optimizer
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from .particle import Particle
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from .particle import Particle
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__version__ = "0.1.5"
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__version__ = "0.1.6"
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__all__ = [
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__all__ = [
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"Optimizer",
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"Optimizer",
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@@ -95,16 +95,14 @@ class Optimizer:
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print(f"start running time : {self.day}")
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print(f"start running time : {self.day}")
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for i in tqdm(range(self.n_particles), desc="Initializing Particles"):
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for i in tqdm(range(self.n_particles), desc="Initializing Particles"):
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m = keras.models.model_from_json(model.to_json())
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model_ = keras.models.model_from_json(model.to_json())
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init_weights = m.get_weights()
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w_, sh_, len_ = self._encode(model_.get_weights())
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w_, sh_, len_ = self._encode(init_weights)
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w_ = np.random.uniform(particle_min, particle_max, len(w_))
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w_ = np.random.uniform(particle_min, particle_max, len(w_))
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m.set_weights(self._decode(w_, sh_, len_))
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model_.set_weights(self._decode(w_, sh_, len_))
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m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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model_.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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self.particles[i] = Particle(
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self.particles[i] = Particle(
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m,
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model_,
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loss,
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loss,
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negative=True if i < negative_swarm * self.n_particles else False,
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negative=True if i < negative_swarm * self.n_particles else False,
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mutation=mutation_swarm,
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mutation=mutation_swarm,
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@@ -112,6 +110,9 @@ class Optimizer:
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if i < negative_swarm * self.n_particles:
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if i < negative_swarm * self.n_particles:
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negative_count += 1
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negative_count += 1
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# del m, init_weights, w_, sh_, len_
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# del m, init_weights, w_, sh_, len_
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gc.collect()
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tf.keras.backend.reset_uids()
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tf.keras.backend.clear_session()
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print(f"negative swarm : {negative_count} / {self.n_particles}")
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print(f"negative swarm : {negative_count} / {self.n_particles}")
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print(f"mutation swarm : {mutation_swarm * 100}%")
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print(f"mutation swarm : {mutation_swarm * 100}%")
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@@ -202,7 +203,7 @@ class Optimizer:
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(float): 목적 함수 값
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(float): 목적 함수 값
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"""
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"""
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self.model.set_weights(weights)
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self.model.set_weights(weights)
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self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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# self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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score = self.model.evaluate(x, y, verbose=0)[1]
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score = self.model.evaluate(x, y, verbose=0)[1]
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if score > 0:
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if score > 0:
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return 1 / (1 + score)
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return 1 / (1 + score)
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@@ -252,11 +253,8 @@ class Optimizer:
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self.train_summary_writer[i] = tf.summary.create_file_writer(
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self.train_summary_writer[i] = tf.summary.create_file_writer(
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train_log_dir + f"/{i}"
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train_log_dir + f"/{i}"
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)
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)
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except AssertionError as e:
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print(e)
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elif check_point is not None or log == 1:
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sys.exit(1)
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try:
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if check_point is not None or log == 1:
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if save_path is None:
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if save_path is None:
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raise ValueError("save_path is None")
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raise ValueError("save_path is None")
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else:
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else:
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@@ -290,12 +288,6 @@ class Optimizer:
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self.g_best = p.get_best_weights()
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self.g_best = p.get_best_weights()
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self.g_best_ = p.get_best_weights()
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self.g_best_ = p.get_best_weights()
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if local_score[0] == None:
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local_score[0] = np.inf
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if local_score[1] == None:
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local_score[1] = 0
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if log == 1:
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if log == 1:
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with open(
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with open(
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f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv",
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f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv",
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@@ -306,10 +298,12 @@ class Optimizer:
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f.write(", ")
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f.write(", ")
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else:
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else:
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f.write("\n")
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f.write("\n")
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if log == 2:
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elif log == 2:
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with self.train_summary_writer[i].as_default():
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with self.train_summary_writer[i].as_default():
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tf.summary.scalar("loss", local_score[0], step=0)
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tf.summary.scalar("loss", local_score[0], step=0)
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tf.summary.scalar("accuracy", local_score[1], step=0)
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tf.summary.scalar("accuracy", local_score[1], step=0)
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del local_score
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del local_score
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gc.collect()
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gc.collect()
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tf.keras.backend.reset_uids()
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tf.keras.backend.reset_uids()
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@@ -107,7 +107,7 @@ class Particle:
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Returns:
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Returns:
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(float): 점수
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(float): 점수
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"""
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"""
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self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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# self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
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score = self.model.evaluate(x, y, verbose=0, use_multiprocessing=True)
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score = self.model.evaluate(x, y, verbose=0, use_multiprocessing=True)
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if renewal == "acc":
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if renewal == "acc":
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if score[1] > self.best_score:
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if score[1] > self.best_score:
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620
pso2keras.ipynb
620
pso2keras.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4",
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"authorship_tag": "ABX9TyNDijdc1kgN6OY64Tq8UGQH",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU",
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"widgets": {
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"source": [
|
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||||||
"<a href=\"https://colab.research.google.com/github/jung-geun/PSO/blob/master/pso2keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
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"import sys\n",
|
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"print('python version ', sys.version)\n",
|
|
||||||
"\n",
|
|
||||||
"# !pip uninstall pso2keras\n",
|
|
||||||
"!pip install --upgrade pip\n",
|
|
||||||
"!pip install pso2keras==0.1.5"
|
|
||||||
],
|
|
||||||
"metadata": {
|
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"colab": {
|
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|
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},
|
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"id": "Qd4s8Pu0nYGs",
|
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|
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},
|
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|
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"outputs": [
|
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{
|
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"output_type": "stream",
|
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"name": "stdout",
|
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||||||
"text": [
|
|
||||||
"python version 3.10.6 (main, May 29 2023, 11:10:38) [GCC 11.3.0]\n",
|
|
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"Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.2)\n",
|
|
||||||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
|
||||||
"\u001b[0mCollecting pso2keras==0.1.5\n",
|
|
||||||
" Obtaining dependency information for pso2keras==0.1.5 from https://files.pythonhosted.org/packages/cc/e9/6694b997be42496d097288cad18e140fc083221b581b5bba4d7c187b48cd/pso2keras-0.1.5-py3-none-any.whl.metadata\n",
|
|
||||||
" Downloading pso2keras-0.1.5-py3-none-any.whl.metadata (8.6 kB)\n",
|
|
||||||
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (4.65.0)\n",
|
|
||||||
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (1.22.4)\n",
|
|
||||||
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (1.5.3)\n",
|
|
||||||
"Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (7.34.0)\n",
|
|
||||||
"Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (67.7.2)\n",
|
|
||||||
"Collecting jedi>=0.16 (from ipython->pso2keras==0.1.5)\n",
|
|
||||||
" Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
|
|
||||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|
||||||
"\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (4.4.2)\n",
|
|
||||||
"Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.7.5)\n",
|
|
||||||
"Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (5.7.1)\n",
|
|
||||||
"Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (3.0.39)\n",
|
|
||||||
"Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (2.14.0)\n",
|
|
||||||
"Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.2.0)\n",
|
|
||||||
"Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.1.6)\n",
|
|
||||||
"Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (4.8.0)\n",
|
|
||||||
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pso2keras==0.1.5) (2.8.2)\n",
|
|
||||||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pso2keras==0.1.5) (2022.7.1)\n",
|
|
||||||
"Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->pso2keras==0.1.5) (0.8.3)\n",
|
|
||||||
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->pso2keras==0.1.5) (0.7.0)\n",
|
|
||||||
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->pso2keras==0.1.5) (0.2.6)\n",
|
|
||||||
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->pso2keras==0.1.5) (1.16.0)\n",
|
|
||||||
"Downloading pso2keras-0.1.5-py3-none-any.whl (11 kB)\n",
|
|
||||||
"Installing collected packages: jedi, pso2keras\n",
|
|
||||||
" Attempting uninstall: pso2keras\n",
|
|
||||||
" Found existing installation: pso2keras 0.1.0\n",
|
|
||||||
" Uninstalling pso2keras-0.1.0:\n",
|
|
||||||
" Successfully uninstalled pso2keras-0.1.0\n",
|
|
||||||
"Successfully installed jedi-0.18.2 pso2keras-0.1.5\n",
|
|
||||||
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
|
||||||
"\u001b[0m"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {
|
|
||||||
"colab": {
|
|
||||||
"base_uri": "https://localhost:8080/",
|
|
||||||
"height": 458,
|
|
||||||
"referenced_widgets": [
|
|
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"52249d81446e4ae29b376ade96d15636",
|
|
||||||
"6dafb96228714fc19c62b97d43c3835c",
|
|
||||||
"a1c7d30b133c4015a7f6ae5aa9b79705",
|
|
||||||
"32b01d1a0d9c4c27b1f4862479e96c86",
|
|
||||||
"f44dfe5c3d7d48f99a7071c94ee61b0b",
|
|
||||||
"e1622198e36d4d23a87ddc05d4466713",
|
|
||||||
"3ebf57ddd70642ef8cbda4b25a375e64",
|
|
||||||
"b517359a6556481392beb0744b6b7127",
|
|
||||||
"2dc41c24eb5243dbbcff659b6e635da1",
|
|
||||||
"85347ee2ed11446ba3a5a4b39578b1b8",
|
|
||||||
"6174b44f218448c98098c7bde09f3fcf"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"id": "bVWF-rQ3j_ld",
|
|
||||||
"outputId": "303830cd-14b4-4fe5-f75c-a5280ad03f35"
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"output_type": "stream",
|
|
||||||
"name": "stdout",
|
|
||||||
"text": [
|
|
||||||
"x_test : (28, 28, 1) | y_test : (10,)\n",
|
|
||||||
"start running time : 20230721-063118\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"output_type": "display_data",
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"Initializing Particles: 0%| | 0/100 [00:00<?, ?it/s]"
|
|
||||||
],
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0,
|
|
||||||
"model_id": "52249d81446e4ae29b376ade96d15636"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"metadata": {}
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"output_type": "error",
|
|
||||||
"ename": "KeyboardInterrupt",
|
|
||||||
"evalue": "ignored",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|
||||||
"\u001b[0;32m<ipython-input-5-3fcd5b26d5e8>\u001b[0m in \u001b[0;36m<cell line: 73>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean_squared_error'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m pso_mnist = Optimizer(\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pso/optimizer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, loss, n_particles, c0, c1, w_min, w_max, negative_swarm, mutation_swarm, np_seed, tf_seed, random_state, particle_min, particle_max)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mw_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msh_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mw_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparticle_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparticle_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msh_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36mset_weights\u001b[0;34m(self, weights)\u001b[0m\n\u001b[1;32m 1833\u001b[0m \u001b[0mweight_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1834\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1835\u001b[0;31m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;31m# Perform any layer defined finalization of the layer state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mop_dispatch_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[0;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1175\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1177\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m 4310\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtuples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4311\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4312\u001b[0;31m \u001b[0m_assign_value_to_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4313\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4314\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/backend.py\u001b[0m in \u001b[0;36m_assign_value_to_variable\u001b[0;34m(variable, value)\u001b[0m\n\u001b[1;32m 4358\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4359\u001b[0m \u001b[0;31m# For the normal tf.Variable assign\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4360\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, value, use_locking, name, read_value)\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;31m# initialize the variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_handle_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m \u001b[0mvalue_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 975\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 976\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/profiler/trace.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtrace_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m 1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1641\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1642\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/tensor_conversion_registry.py\u001b[0m in \u001b[0;36m_default_conversion_function\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_default_conversion_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mas_ref\u001b[0m \u001b[0;31m# Unused.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcalled\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0msymbolic\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0m\u001b[1;32m 269\u001b[0m allow_broadcast=True)\n\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf.constant\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_eager_impl\u001b[0;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;34m\"\"\"Creates a constant on the current device.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 306\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# %%\n",
|
|
||||||
"import os\n",
|
|
||||||
"import sys\n",
|
|
||||||
"\n",
|
|
||||||
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
|
|
||||||
"\n",
|
|
||||||
"import gc\n",
|
|
||||||
"\n",
|
|
||||||
"import tensorflow as tf\n",
|
|
||||||
"from keras.datasets import mnist\n",
|
|
||||||
"from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
|
|
||||||
"from keras.models import Sequential\n",
|
|
||||||
"from tensorflow import keras\n",
|
|
||||||
"\n",
|
|
||||||
"from pso import Optimizer\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def get_data():\n",
|
|
||||||
" (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
|
|
||||||
"\n",
|
|
||||||
" x_train, x_test = x_train / 255.0, x_test / 255.0\n",
|
|
||||||
" x_train = x_train.reshape((60000, 28, 28, 1))\n",
|
|
||||||
" x_test = x_test.reshape((10000, 28, 28, 1))\n",
|
|
||||||
"\n",
|
|
||||||
" y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)\n",
|
|
||||||
"\n",
|
|
||||||
" x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)\n",
|
|
||||||
" y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)\n",
|
|
||||||
"\n",
|
|
||||||
" print(f\"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}\")\n",
|
|
||||||
" print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n",
|
|
||||||
"\n",
|
|
||||||
" return x_train, y_train, x_test, y_test\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def get_data_test():\n",
|
|
||||||
" (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
|
|
||||||
" x_test = x_test / 255.0\n",
|
|
||||||
" x_test = x_test.reshape((10000, 28, 28, 1))\n",
|
|
||||||
"\n",
|
|
||||||
" y_test = tf.one_hot(y_test, 10)\n",
|
|
||||||
"\n",
|
|
||||||
" x_test = tf.convert_to_tensor(x_test)\n",
|
|
||||||
" y_test = tf.convert_to_tensor(y_test)\n",
|
|
||||||
"\n",
|
|
||||||
" print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n",
|
|
||||||
"\n",
|
|
||||||
" return x_test, y_test\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def make_model():\n",
|
|
||||||
" model = Sequential()\n",
|
|
||||||
" model.add(\n",
|
|
||||||
" Conv2D(32, kernel_size=(5, 5), activation=\"relu\", input_shape=(28, 28, 1))\n",
|
|
||||||
" )\n",
|
|
||||||
" model.add(MaxPooling2D(pool_size=(3, 3)))\n",
|
|
||||||
" model.add(Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n",
|
|
||||||
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
|
|
||||||
" model.add(Dropout(0.25))\n",
|
|
||||||
" model.add(Flatten())\n",
|
|
||||||
" model.add(Dense(128, activation=\"relu\"))\n",
|
|
||||||
" model.add(Dense(10, activation=\"softmax\"))\n",
|
|
||||||
"\n",
|
|
||||||
" return model\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"# %%\n",
|
|
||||||
"model = make_model()\n",
|
|
||||||
"x_train, y_train = get_data_test()\n",
|
|
||||||
"\n",
|
|
||||||
"loss = 'mean_squared_error'\n",
|
|
||||||
"\n",
|
|
||||||
"pso_mnist = Optimizer(\n",
|
|
||||||
" model,\n",
|
|
||||||
" loss=loss,\n",
|
|
||||||
" n_particles=100,\n",
|
|
||||||
" c0=0.3,\n",
|
|
||||||
" c1=0.5,\n",
|
|
||||||
" w_min=0.4,\n",
|
|
||||||
" w_max=0.7,\n",
|
|
||||||
" negative_swarm=0.1,\n",
|
|
||||||
" mutation_swarm=0.2,\n",
|
|
||||||
" particle_min=-5,\n",
|
|
||||||
" particle_max=5,\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"best_score = pso_mnist.fit(\n",
|
|
||||||
" x_train,\n",
|
|
||||||
" y_train,\n",
|
|
||||||
" epochs=200,\n",
|
|
||||||
" save_info=True,\n",
|
|
||||||
" log=2,\n",
|
|
||||||
" log_name=\"mnist\",\n",
|
|
||||||
" save_path=\"./result/mnist\",\n",
|
|
||||||
" renewal=\"acc\",\n",
|
|
||||||
" check_point=25,\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"print(\"Done!\")\n",
|
|
||||||
"\n",
|
|
||||||
"gc.collect()\n",
|
|
||||||
"sys.exit(0)\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user