# episode = []
for t in range(MAX_STEPS):
# env.render()
# action = pg_reinforce.sampleAction(state[np.newaxis,:])
#Choose action
rand = np.random.rand()
if rand > .9:
a_t = env.action_space.sample()
else:
data_x_0 = np.array([np.concatenate([s_t,np.array([0.])])])
data_x_1 = np.array([np.concatenate([s_t,np.array([1.])])])
q0 = model.forward(data_x_0).data.numpy()
q1 = model.forward(data_x_1).data.numpy()
if q0 > q1:
a_t = 0
else:
a_t = 1
#Apply action to environment
s_tp1, reward, done, _ = env.step(a_t)
total_rewards += reward
# r_t = 0 if done else 0.1 # normalize reward
if done:
r_t = 0
for i_episode in range(MAX_EPISODES):
s_t = env.reset()
total_rewards = 0
for t in range(MAX_STEPS):
# env.render()
# action = pg_reinforce.sampleAction(state[np.newaxis,:])
rand = np.random.rand()
if rand > .9:
a_t = env.action_space.sample()
else:
data_x_0 = np.array([np.concatenate([s_t, np.array([0.])])])
data_x_1 = np.array([np.concatenate([s_t, np.array([1.])])])
q0 = model.forward(data_x_0).data.numpy()
q1 = model.forward(data_x_1).data.numpy()
if q0 > q1:
a_t = 0
else:
a_t = 1
# print (t, 'action', action, env.action_space)
# afadsf
s_tp1, reward, done, _ = env.step(a_t)
# print (r_t)
# print t+1, 'state', next_state, 'reward', reward, 'done', done
##################
images_numpy = load_train_images(idx3_ubyte_file=train_images_idx3_ubyte_file)
labels_numpy = load_train_labels(idx1_ubyte_file=train_labels_idx1_ubyte_file)
labels_numpy = one_hot_transformer(labels_numpy)
print('\nThe shape of all data images are:', images_numpy.shape)
print('\nThe shape of all data labels are:', labels_numpy.shape)
images_vectors = images_numpy.reshape((60000, -1)) / 255
fnn = NN()
train = SGD_Train()
train.train_hyperparam['stop_criterion'] = 2
fnn.model_hyperparam['batch_size'] = 128
fnn.model_hyperparam['layer1_dim'] = 134
fnn.model_hyperparam['layer2_dim'] = 34
fnn.model_hyperparam['layer4_dim'] = 10
fnn.model_hyperparam['dropout_percent'] = 0.05
print('\nThe hyperparameters of this fully connected neuron network are:\n',fnn.model_hyperparam)
print('The hyperparameters of training process are:\n',train.train_hyperparam)
train.sgd_train(fnn=fnn ,images_vectors=images_vectors[0:60000] ,labels_numpy=labels_numpy[0:60000])
Accuracy = 0
account = 0
fnn.model_hyperparam['dropout_percent'] = 0
for i in range(10000 // fnn.model_hyperparam['batch_size']):
pred_labels, _ = fnn.forward(images_vectors[50000 + i * fnn.model_hyperparam['batch_size']:50000 + (i + 1) * fnn.model_hyperparam['batch_size']],
labels_numpy[50000 + i * fnn.model_hyperparam['batch_size']:50000 + (i + 1) * fnn.model_hyperparam['batch_size']], if_train=True)
account += np.nonzero(pred_labels - np.nonzero(labels_numpy[50000 + i * fnn.model_hyperparam['batch_size']:
50000 + (i + 1) * fnn.model_hyperparam['batch_size']])[1])[0].shape[0]
print('The accuracy on the whole data set is %f %%:\n' % (100 - 100 * (account / 10000)))
for i_episode in range(MAX_EPISODES):
s_t = env.reset()
total_rewards = 0
for t in range(MAX_STEPS):
# env.render()
# action = pg_reinforce.sampleAction(state[np.newaxis,:])
rand = np.random.rand()
if rand > .9:
a_t = env.action_space.sample()
else:
data_x_0 = np.array([np.concatenate([s_t,np.array([0.])])])
data_x_1 = np.array([np.concatenate([s_t,np.array([1.])])])
q0 = model.forward(data_x_0).data.numpy()
q1 = model.forward(data_x_1).data.numpy()
if q0 > q1:
a_t = 0
else:
a_t = 1
# print (t, 'action', action, env.action_space)
# afadsf
s_tp1, reward, done, _ = env.step(a_t)
# print (r_t)
