tensorflow 相关问题

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python tensorflow gpu

回答 1 投票 0

keras软件包不可用。

tensorflow keras pycharm tensorflow2.0 tf.keras

回答 2 投票 0

我在本机窗户上，我在旧的keras上使用了带有Tensorflow 2.10（GPU加速）的旧凯拉斯。我想尝试使用Pytorch后端的Keras 3。有人可以帮我，为什么这种模型使用Keras 3.4.1和Pytorch 2.3.1后端训练10倍？使用我的GPU，一个时代的TF需要超过2分钟的时间，而Pytorch则超过20分钟。

python performance tensorflow keras pytorch

回答 0 投票 0

tensorflow perfomanceessue

def build_model(): model = keras.Sequential([ layers.Input(shape=(400,)), layers.Dense(128, activation="relu"), layers.Dense(128, activation="relu"), layers.Dense(3) ]) model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="huber") return model class DQNAgent: def __init__(self): self.model = build_model() self.target_model = build_model() self.target_model.set_weights(self.model.get_weights()) self.memory = deque(maxlen=1000) self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.gamma = 0.95 self.batch_size = 32 def choose_action(self, state): if np.random.rand() < self.epsilon: return random.choice([0, 1, 2]) q_values = self.model.predict(np.array([state]), verbose=0) return np.argmax(q_values[0]) def remember(self, state, action, reward, next_state, done): self.memory.append((state, action, reward, next_state, done)) def train(self): if len(self.memory) < self.batch_size: return batch = random.sample(self.memory, self.batch_size) states, targets = [], [] for state, action, reward, next_state, done in batch: target = reward if not done: target += self.gamma * np.max(self.target_model.predict(np.array([next_state]), verbose=0)) q_values = self.model.predict(np.array([state]), verbose=0) q_values[0][action] = target states.append(state) targets.append(q_values[0]) self.model.fit(np.array(states), np.array(targets), epochs=1, verbose=0) if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay def update_target_model(self): self.target_model.set_weights(self.model.get_weights())

python python-3.x tensorflow machine-learning

回答 0 投票 0

eRROR：“用TF-Serving

inceptionv3_model = InceptionV3(weights="imagenet", include_top=False, input_shape=(224, 224, 3)) inceptionv3_model.trainable = False model = Sequential() model.add(inceptionv3_model) model.add(Conv2D(32, (3, 3), activation='relu', padding='same')) model.add(GlobalAveragePooling2D()) # flatten model.add(Flatten()) # hidden layer model.add(Dense(64, activation='relu')) model.add(Dropout(0.2)) # output layer model.add(Dense(3, activation='softmax'))

python docker tensorflow

回答 1 投票 0

在tensorflow中是否有类似的功能，例如pytorch中的load_state_dict（）？

tensorflow neural-network deep-learning pytorch

回答 2 投票 0

自定义损失功能的元素胶带

到目前为止，我有这个代码，但我无法正常工作。我收到的错误，例如：valueError：通过类型“ kerastensor”的对象

tensorflow keras tf.keras autoencoder gradienttape

回答 1 投票 0

没有模块名称'tensorflow'

为运行代码的要求是

python numpy tensorflow visual-studio-code module