我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用utils.one_hot()。
def _get_minibatch_feed_dict(self, target_q_values, non_terminal_minibatch, terminal_minibatch): """ Helper to construct the feed_dict for train_op. Takes the non-terminal and terminal minibatches as well as the max q-values computed from the target network for non-terminal states. Computes the expected q-values based on discounted future reward. @return: feed_dict to be used for train_op """ assert len(target_q_values) == len(non_terminal_minibatch) states = [] expected_q = [] actions = [] # Compute expected q-values to plug into the loss function minibatch = itertools.chain(non_terminal_minibatch, terminal_minibatch) for item, target_q in zip_longest(minibatch, target_q_values, fillvalue=0): state, action, reward, _, _ = item states.append(state) # target_q will be 0 for terminal states due to fillvalue in zip_longest expected_q.append(reward + self.config.reward_discount * target_q) actions.append(utils.one_hot(action, self.env.action_space.n)) return { self.network.x_placeholder: states, self.network.q_placeholder: expected_q, self.network.action_placeholder: actions, }
def gen_onehot_keys(input_size, batch_size, targets): return np.vstack([one_hot(input_size, [t]) for t in targets])
def __init__(self, source): print("Loading in word2vec model") self.vmodel = gs.models.Word2Vec.load('vectors.bin') print("Loading in text") text = load2(source) parsed_words = text.split(" ") code_num = 1 print("Creating word -> vector dictionary...") for word in parsed_words: if not word in word_coding: word_coding[word] = code_num coded_word[code_num] = word code_num += 1 vec_values[word] = self.vmodel[word] coded_vector.append(word_coding[word]) print('Number of distinct words: ', len(word_coding)) sd_size = int(len(coded_vector) / sd_len) x_d = y_d = v_d = i_d = [] for idx in range(0, sd_size - 1): for iidx in range(0, sd_len - 1): indexD = coded_vector[idx * sd_len + iidx + 0:(idx + 1) * sd_len + iidx] i_D.append(indexD) vectorValD = [vec_values[myWord] for myWord in parsed_words[idx * sd_len + iidx + 0:(idx + 1) * sd_len + iidx]] x_D.append(vectorValD) y_D.append(one_hot(coded_vector[(idx + 1) * sd_len + iidx], word_coding)) v_D.append(vec_values[parsed_words[(idx + 1) * sd_len + iidx]]) self.x_d = np.asarray(x_D) self.y_d = np.asarray(y_D) self.v_d = np.asarray(v_D) self.i_d = np.asarray(i_D) print('shapes: ' + str(self.x_d.shape))
def main(): """ Test an RNN trained for TIMIT phoneme recognition. """ args, params_str, layer_kwargs = parse_args() _, _, test_inputs, test_labels = timitphonemerec.load_split(args.data_dir, val=False, mfcc=True, normalize=True) # Input seqs have shape [length, INPUT_SIZE]. Label seqs are int8 arrays with shape [length], # but need to have shape [length, 1] for the batch generator. test_labels = [seq[:, np.newaxis] for seq in test_labels] test_batches = utils.full_bptt_batch_generator(test_inputs, test_labels, TEST_BATCH_SIZE, num_epochs=1, shuffle=False) model = models.RNNClassificationModel(args.layer_type, INPUT_SIZE, TARGET_SIZE, args.num_hidden_units, args.activation_type, **layer_kwargs) def _error_rate(valid_predictions, valid_targets): incorrect_mask = tf.logical_not(tf.equal(tf.argmax(valid_predictions, 1), tf.argmax(valid_targets, 1))) return tf.reduce_mean(tf.to_float(incorrect_mask)) model.error_rate = _error_rate(model.valid_predictions, model.valid_targets) config = tf.ConfigProto() config.gpu_options.allow_growth = False sess = tf.Session(config=config) saver = tf.train.Saver() saver.restore(sess, os.path.join(args.results_dir, 'model.ckpt')) batch_inputs, batch_labels = next(test_batches) batch_targets = utils.one_hot(np.squeeze(batch_labels, 2), TARGET_SIZE) valid_predictions, valid_targets, error_rate = sess.run( [model.valid_predictions, model.valid_targets, model.error_rate], feed_dict={model.inputs: batch_inputs, model.targets: batch_targets} ) print('%f' % error_rate) with open(os.path.join(args.results_dir, 'test_result.txt'), 'w') as f: print('%f' % error_rate, file=f)
def main(): """ Test an RNN for sequential (possibly permuted) MNIST recognition. """ args, params_str, layer_kwargs = parse_args() outs = mnist.load_split(args.data_dir, val=False, permute=args.permute, normalize=True, seed=0) _, _, test_images, test_labels = outs # Flatten the images. test_inputs = test_images.reshape([len(test_images), -1, INPUT_SIZE]) # Align sequence-level labels with the appropriate time steps by padding with NaNs, # and to do so, first convert the labels to floats. length = test_inputs.shape[1] pad = lambda x: np.pad(x, [[0, 0], [length - 1, 0], [0, 0]], mode='constant', constant_values=np.nan) test_labels = pad(test_labels.reshape([-1, 1, 1]).astype(np.float)) test_batches = utils.full_bptt_batch_generator(test_inputs, test_labels, TEST_BATCH_SIZE, num_epochs=1, shuffle=False) model = models.RNNClassificationModel(args.layer_type, INPUT_SIZE, TARGET_SIZE, args.num_hidden_units, args.activation_type, **layer_kwargs) def _error_rate(valid_predictions, valid_targets): incorrect_mask = tf.logical_not(tf.equal(tf.argmax(valid_predictions, 1), tf.argmax(valid_targets, 1))) return tf.reduce_mean(tf.to_float(incorrect_mask)) model.error_rate = _error_rate(model.valid_predictions, model.valid_targets) config = tf.ConfigProto() config.gpu_options.allow_growth = False sess = tf.Session(config=config) saver = tf.train.Saver() saver.restore(sess, os.path.join(args.results_dir, 'model.ckpt')) error_rates = [] for batch_inputs, batch_labels in test_batches: batch_targets = utils.one_hot(np.squeeze(batch_labels, 2), TARGET_SIZE) valid_predictions, valid_targets, batch_error_rates = sess.run( [model.valid_predictions, model.valid_targets, model.error_rate], feed_dict={model.inputs: batch_inputs, model.targets: batch_targets} ) error_rates.append(batch_error_rates) error_rate = np.mean(error_rates, dtype=np.float) print('%f' % error_rate) with open(os.path.join(args.results_dir, 'test_result.txt'), 'w') as f: print('%f' % error_rate, file=f)
