Python keras.optimizers.RMSprop() Examples
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Example #1
Source File: train_agent_kerasrl.py From gym-malware with MIT License | 7 votes |
def train_dqn_model(layers, rounds=10000, run_test=False, use_score=False): ENV_NAME = 'malware-score-v0' if use_score else 'malware-v0' env = gym.make(ENV_NAME) env.seed(123) nb_actions = env.action_space.n window_length = 1 # "experience" consists of where we were, where we are now # generate a policy model model = generate_dense_model((window_length,) + env.observation_space.shape, layers, nb_actions) # configure and compile our agent # BoltzmannQPolicy selects an action stochastically with a probability generated by soft-maxing Q values policy = BoltzmannQPolicy() # memory can help a model during training # for this, we only consider a single malware sample (window_length=1) for each "experience" memory = SequentialMemory(limit=32, ignore_episode_boundaries=False, window_length=window_length) # DQN agent as described in Mnih (2013) and Mnih (2015). # http://arxiv.org/pdf/1312.5602.pdf # http://arxiv.org/abs/1509.06461 agent = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=16, enable_double_dqn=True, enable_dueling_network=True, dueling_type='avg', target_model_update=1e-2, policy=policy, batch_size=16) # keras-rl allows one to use and built-in keras optimizer agent.compile(RMSprop(lr=1e-3), metrics=['mae']) # play the game. learn something! agent.fit(env, nb_steps=rounds, visualize=False, verbose=2) history_train = env.history history_test = None if run_test: # Set up the testing environment TEST_NAME = 'malware-score-test-v0' if use_score else 'malware-test-v0' test_env = gym.make(TEST_NAME) # evaluate the agent on a few episodes, drawing randomly from the test samples agent.test(test_env, nb_episodes=100, visualize=False) history_test = test_env.history return agent, model, history_train, history_test
Example #2
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #3
Source File: breakout_dqn.py From reinforcement-learning with MIT License | 6 votes |
def optimizer(self): a = K.placeholder(shape=(None,), dtype='int32') y = K.placeholder(shape=(None,), dtype='float32') py_x = self.model.output a_one_hot = K.one_hot(a, self.action_size) q_value = K.sum(py_x * a_one_hot, axis=1) error = K.abs(y - q_value) quadratic_part = K.clip(error, 0.0, 1.0) linear_part = error - quadratic_part loss = K.mean(0.5 * K.square(quadratic_part) + linear_part) optimizer = RMSprop(lr=0.00025, epsilon=0.01) updates = optimizer.get_updates(self.model.trainable_weights, [], loss) train = K.function([self.model.input, a, y], [loss], updates=updates) return train # approximate Q function using Convolution Neural Network # state is input and Q Value of each action is output of network
Example #4
Source File: breakout_a3c.py From reinforcement-learning with MIT License | 6 votes |
def actor_optimizer(self): action = K.placeholder(shape=[None, self.action_size]) advantages = K.placeholder(shape=[None, ]) policy = self.actor.output good_prob = K.sum(action * policy, axis=1) eligibility = K.log(good_prob + 1e-10) * advantages actor_loss = -K.sum(eligibility) entropy = K.sum(policy * K.log(policy + 1e-10), axis=1) entropy = K.sum(entropy) loss = actor_loss + 0.01*entropy optimizer = RMSprop(lr=self.actor_lr, rho=0.99, epsilon=0.01) updates = optimizer.get_updates(self.actor.trainable_weights, [], loss) train = K.function([self.actor.input, action, advantages], [loss], updates=updates) return train # make loss function for Value approximation
Example #5
Source File: breakout_dueling_ddqn.py From reinforcement-learning with MIT License | 6 votes |
def optimizer(self): a = K.placeholder(shape=(None, ), dtype='int32') y = K.placeholder(shape=(None, ), dtype='float32') py_x = self.model.output a_one_hot = K.one_hot(a, self.action_size) q_value = K.sum(py_x * a_one_hot, axis=1) error = K.abs(y - q_value) quadratic_part = K.clip(error, 0.0, 1.0) linear_part = error - quadratic_part loss = K.mean(0.5 * K.square(quadratic_part) + linear_part) optimizer = RMSprop(lr=0.00025, epsilon=0.01) updates = optimizer.get_updates(self.model.trainable_weights, [], loss) train = K.function([self.model.input, a, y], [loss], updates=updates) return train # approximate Q function using Convolution Neural Network # state is input and Q Value of each action is output of network # dueling network's Q Value is sum of advantages and state value
Example #6
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #7
Source File: trainer.py From image-segmentation with MIT License | 6 votes |
def get_optimizer(config): if config.OPTIMIZER == 'SGD': return SGD(lr=config.LEARNING_RATE, momentum=config.LEARNING_MOMENTUM, clipnorm=config.GRADIENT_CLIP_NORM, nesterov=config.NESTEROV) elif config.OPTIMIZER == 'RMSprop': return RMSprop(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM) elif config.OPTIMIZER == 'Adagrad': return Adagrad(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM) elif config.OPTIMIZER == 'Adadelta': return Adadelta(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM) elif config.OPTIMIZER == 'Adam': return Adam(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM, amsgrad=config.AMSGRAD) elif config.OPTIMIZER == 'Adamax': return Adamax(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM) elif config.OPTIMIZER == 'Nadam': return Nadam(lr=config.LEARNING_RATE, clipnorm=config.GRADIENT_CLIP_NORM) else: raise Exception('Unrecognized optimizer: {}'.format(config.OPTIMIZER))
Example #8
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #9
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #10
Source File: ir2tagsets_seq.py From plastering with MIT License | 6 votes |
def fit_new(self, x, y=None): timesteps = x.shape[1] input_dim = x.shape[2] self.ae = Sequential() self.ae.add(Dense(self.latent_dim, input_shape=(timesteps,input_dim,), activation='relu', name='enc')) self.ae.add(Dropout(0.2)) self.ae.add(Dense(input_dim, activation='softmax', name='dec')) self.encoder = Model(inputs=self.ae.input, outputs=self.ae.get_layer('enc').output) #rmsprop = RMSprop(lr=0.05) self.ae.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'],) self.ae.fit(x, x, epochs=1)
Example #11
Source File: cnn_mnist.py From deep_learning_ex with MIT License | 6 votes |
def init_model(): """ """ start_time = time.time() print 'Compiling model...' model = Sequential() model.add(Convolution2D(64, 3,3, border_mode='valid', input_shape=INPUT_SHAPE)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(.25)) model.add(Flatten()) model.add(Dense(10)) model.add(Activation('softmax')) rms = RMSprop() model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy']) print 'Model compiled in {0} seconds'.format(time.time() - start_time) model.summary() return model
Example #12
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #13
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #14
Source File: ir2tagsets_seq.py From plastering with MIT License | 6 votes |
def fit_dep(self, x, y=None): timesteps = x.shape[1] input_dim = x.shape[2] inputs = Input(shape=(timesteps, input_dim)) encoded = LSTM(self.latent_dim)(inputs) decoded = RepeatVector(timesteps)(encoded) decoded = LSTM(input_dim, return_sequences=True)(decoded) encoded_input = Input(shape=(self.latent_dim,)) self.sequence_autoencoder = Model(inputs, decoded) self.encoder = Model(inputs, encoded) self.sequence_autoencoder.compile( #loss='binary_crossentropy', loss='categorical_crossentropy', optimizer='RMSprop', metrics=['binary_accuracy'] ) self.sequence_autoencoder.fit(x, x)
Example #15
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #16
Source File: optimizers.py From Attention-Based-Aspect-Extraction with Apache License 2.0 | 6 votes |
def get_optimizer(args): clipvalue = 0 clipnorm = 10 if args.algorithm == 'rmsprop': optimizer = opt.RMSprop(lr=0.001, rho=0.9, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'sgd': optimizer = opt.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adagrad': optimizer = opt.Adagrad(lr=0.01, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adadelta': optimizer = opt.Adadelta(lr=1.0, rho=0.95, epsilon=1e-06, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adam': optimizer = opt.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=clipnorm, clipvalue=clipvalue) elif args.algorithm == 'adamax': optimizer = opt.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=clipnorm, clipvalue=clipvalue) return optimizer
Example #17
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #18
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #19
Source File: siamese.py From DogEmbeddings with MIT License | 6 votes |
def SiameseNetwork(input_shape=(5880,)): base_network = create_base_network(input_shape) input_a = Input(shape=input_shape) input_b = Input(shape=input_shape) processed_a = base_network(input_a) processed_b = base_network(input_b) distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b]) model = Model([input_a, input_b], distance) rms = RMSprop() model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy]) return model, base_network
Example #20
Source File: train_network.py From A3C_Keras_FlappyBird with MIT License | 6 votes |
def buildmodel(): print("Model building begins") model = Sequential() keras.initializers.RandomUniform(minval=-0.1, maxval=0.1, seed=None) S = Input(shape = (IMAGE_ROWS, IMAGE_COLS, IMAGE_CHANNELS, ), name = 'Input') h0 = Convolution2D(16, kernel_size = (8,8), strides = (4,4), activation = 'relu', kernel_initializer = 'random_uniform', bias_initializer = 'random_uniform')(S) h1 = Convolution2D(32, kernel_size = (4,4), strides = (2,2), activation = 'relu', kernel_initializer = 'random_uniform', bias_initializer = 'random_uniform')(h0) h2 = Flatten()(h1) h3 = Dense(256, activation = 'relu', kernel_initializer = 'random_uniform', bias_initializer = 'random_uniform') (h2) P = Dense(1, name = 'o_P', activation = 'sigmoid', kernel_initializer = 'random_uniform', bias_initializer = 'random_uniform') (h3) V = Dense(1, name = 'o_V', kernel_initializer = 'random_uniform', bias_initializer = 'random_uniform') (h3) model = Model(inputs = S, outputs = [P,V]) rms = RMSprop(lr = LEARNING_RATE, rho = 0.99, epsilon = 0.1) model.compile(loss = {'o_P': logloss, 'o_V': sumofsquares}, loss_weights = {'o_P': 1., 'o_V' : 0.5}, optimizer = rms) return model #function to preprocess an image before giving as input to the neural network
Example #21
Source File: ff_mnist.py From deep_learning_ex with MIT License | 6 votes |
def init_model(): start_time = time.time() print 'Compiling Model ... ' model = Sequential() model.add(Dense(500, input_dim=784)) model.add(Activation('relu')) model.add(Dropout(0.4)) model.add(Dense(300)) model.add(Activation('relu')) model.add(Dropout(0.4)) model.add(Dense(10)) model.add(Activation('softmax')) rms = RMSprop() model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy']) print 'Model compiled in {0} seconds'.format(time.time() - start_time) return model
Example #22
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #23
Source File: test_keras.py From docker-python with Apache License 2.0 | 6 votes |
def test_train(self): train = pd.read_csv("/input/tests/data/train.csv") x_train = train.iloc[:,1:].values.astype('float32') y_train = to_categorical(train.iloc[:,0].astype('int32')) model = Sequential() model.add(Dense(units=10, input_dim=784, activation='softmax')) model.compile( loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['accuracy']) model.fit(x_train, y_train, epochs=1, batch_size=32) # Uses convnet which depends on libcudnn when running on GPU
Example #24
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #25
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #26
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_lambda_custom_objects(): inputs = Input(shape=(3,)) x = Lambda(lambda x: square_fn(x), output_shape=(3,))(inputs) outputs = Dense(3)(x) model = Model(inputs, outputs) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'square_fn': square_fn}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example #27
Source File: train.py From async-rl with MIT License | 6 votes |
def __init__(self, action_space, batch_size=32, screen=(84, 84), swap_freq=200): from keras.optimizers import RMSprop # ----- self.screen = screen self.input_depth = 1 self.past_range = 3 self.observation_shape = (self.input_depth * self.past_range,) + self.screen self.batch_size = batch_size _, _, self.train_net, adventage = build_network(self.observation_shape, action_space.n) self.train_net.compile(optimizer=RMSprop(epsilon=0.1, rho=0.99), loss=[value_loss(), policy_loss(adventage, args.beta)]) self.pol_loss = deque(maxlen=25) self.val_loss = deque(maxlen=25) self.values = deque(maxlen=25) self.entropy = deque(maxlen=25) self.swap_freq = swap_freq self.swap_counter = self.swap_freq self.unroll = np.arange(self.batch_size) self.targets = np.zeros((self.batch_size, action_space.n)) self.counter = 0
Example #28
Source File: train.py From async-rl with MIT License | 6 votes |
def __init__(self, action_space, batch_size=32, screen=(84, 84), swap_freq=200): from keras.optimizers import RMSprop # ----- self.screen = screen self.input_depth = 1 self.past_range = 3 self.observation_shape = (self.input_depth * self.past_range,) + self.screen self.batch_size = batch_size self.action_value = build_network(self.observation_shape, action_space.n) self.action_value.compile(optimizer=RMSprop(clipnorm=1.), loss='mse') self.losses = deque(maxlen=25) self.q_values = deque(maxlen=25) self.swap_freq = swap_freq self.swap_counter = self.swap_freq self.unroll = np.arange(self.batch_size) self.frames = 0
Example #29
Source File: train.py From async-rl with MIT License | 6 votes |
def __init__(self, action_space, screen=(84, 84), n_step=8, discount=0.99): from keras.optimizers import RMSprop # ----- self.screen = screen self.input_depth = 1 self.past_range = 3 self.observation_shape = (self.input_depth * self.past_range,) + self.screen self.action_value = build_network(self.observation_shape, action_space.n) self.action_value.compile(optimizer=RMSprop(clipnorm=1.), loss='mse') # clipnorm=1. self.action_space = action_space self.observations = np.zeros(self.observation_shape) self.last_observations = np.zeros_like(self.observations) # ----- self.n_step_observations = deque(maxlen=n_step) self.n_step_actions = deque(maxlen=n_step) self.n_step_rewards = deque(maxlen=n_step) self.n_step = n_step self.discount = discount self.counter = 0
Example #30
Source File: test_model_saving.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def test_saving_custom_activation_function(): x = Input(shape=(3,)) output = Dense(3, activation=K.cos)(x) model = Model(x, output) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), 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) model = load_model(fname, custom_objects={'cos': K.cos}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)