Python get_robert_frost Examples

Example #1

def train_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(30, 30, len(word2idx))
    rnn.fit(sentences,
            learning_rate=1e-4,
            show_fig=True,
            activation=T.nnet.relu,
            epochs=2000)
    rnn.save('RNN_D30_M30_epochs2000_relu.npz')

Example #2

def train_poetry():
    # students: tanh didn't work but you should try it
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(50, 50, len(word2idx))
    rnn.fit(sentences,
            learning_rate=10e-5,
            show_fig=True,
            activation=T.nnet.relu,
            epochs=2000)
    rnn.save('RRNN_D50_M50_epochs2000_relu.npz')

Example #3

File: 4_23_rrnn_poetry.py Project: yigher/deep_learning

def train_poetry():
    sentences, word2idx = get_robert_frost()
    # embedding size = 50, and hidden neurons = 50
    rnn = SimpleRNN(50, 50, len(word2idx))
    rnn.fit(sentences,
            learning_rate=10e-5,
            show_fig=False,
            activation=T.nnet.relu,
            epochs=200)
    rnn.save('RRNN_D50_M50_epochs200_relu.npz')

Example #4

def train_poetry():
    sentences, word2idx = get_robert_frost()

    rnn = SimpleRNN(50, 50, len(word2idx))
    rnn.fit(sentences,
            leraning_rate=1e-4,
            show_fig=True,
            activation=T.nnet.relu,
            epochs=2000)
    rnn.save('./rnn_class/RRNN_D50_M50_epochs2000_relu.npz')

Example #5

File: srn_language.py Project: lambdaofgod/machine_learning_examples

def train_poetry(epochs=200, learning_rate=10e-5, mu=0.9, show_fig=True):
    # students: tanh didn't work but you should try it
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(30, 30, len(word2idx))
    rnn.fit(sentences,
            learning_rate=learning_rate,
            mu=mu,
            show_fig=show_fig,
            activation=T.nnet.relu,
            epochs=epochs)
    rnn.save('RNN_D30_M30_epochs{}_relu.npz'.format(epochs))

Example #6

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load("RRNN_D30_M30_epochs1000_relu.npz", activation=T.nnet.relu)

    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi/=pi.sum()

    rnn.generate(pi, word2idx)

Example #7

File: srn_language.py Project: ashishlal/machine_learning_examples

def generate_poetry():
	sentences, word2idx = get_robert_frost()
	rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu.npz', T.nnet.relu)

	V = len(word2idx)
	pi = np.zeros(V)
	for sentence in sentences:
		pi[sentence[0]] += 1
	pi /= pi.sum()

	rnn.generate(pi, word2idx)

Example #8

File: srn_language.py Project: CesarChaMal/machine_learning_examples

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu.npz', T.nnet.relu)

    # determine initial state distribution for starting sentences
    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #9

def train_poetry():
    sentences, word2idx = get_robert_frost()
    D = 30
    M = 50
    epochs = 500
    srn = SRN(D, M, len(word2idx))
    srn.fit(sentences,
            learning_rate=10e-5,
            show_fig=True,
            activation=T.nnet.relu,
            epochs=epochs)
    srn.save('RNN_D%d_M%d_epochs%d_relu.npz' % (D, M, epochs))

Example #10

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RNN_D30_M30_epochs200_relu.npz', T.nnet.relu)

    # Create the initial word distribution.
    V = len(word2idx)
    pi = np.zeros(V)  # creates a vector of length V.
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #11

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu.npz', T.nnet.relu)

    # determine initial state distribution for starting sentences
    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #12

File: srn_language_tf.py Project: Sebster10/Pandas-Practice

def generate_poetry(session, savefile):
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load(savefile, tf.nn.relu, session)

    # determine initial state distribution for starting sentences
    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #13

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    srn = SRN.load('RNN_D30_M30_epochs300_relu.npz', T.nnet.relu)

    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1

    pi /= pi.sum()

    srn.generate(pi, word2idx)

Example #14

File: 3_18_rnn_poetry.py Project: yigher/deep_learning

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RNN_D30_M30_epochs200_relu.npz', T.nnet.relu)

    V = len(word2idx)
    pi = np.zeros(V)
    # create the word distribution
    for sentence in sentences:
        # get first word's frequency count
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #15

File: my_copied_srn_rnn.py Project: RKorzeniowski/Lazy_programmer_projects

def generate_potery():
    sentences, word2idx = get_robert_frost()

    rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu_my.npz',
                         T.nnet.relu)  # sprawdzic czy dziala tez bez
    #rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu_my', T.nnet.relu)
    V = len(word2idx)
    pi = np.zeros(V)
    for sentence in sentences:
        pi[sentence[0]] += 1
    pi /= pi.sum()

    rnn.generate(pi, word2idx)

Example #16

File: generate_poetry_batch.py Project: yigher/deep_learning

def generate_poetry_batches(epochs=500):
    """generate_poetry_batches"""
    sentences, word2idx = get_robert_frost()
    idx2word = {v:k for k, v in word2idx.items()}
    # total number of sentences
    n_sentences = len(sentences)
    # total number of words in corpus
    n_total = sum((len(sentence) + 1) for sentence in sentences)
    for i in range(epochs):
        for j in range(n_sentences):
            if np.random.random() < 0.1:
                print("generating an END to START")
                input_sequence = [0] + sentences[j]
                # [1] is the end token
                output_sequence = sentences[j] + [1]
            else:
                # input sequence is from the start to 2nd last word of X
                # so that the last word can be predicted
                input_sequence = [0] + sentences[j][:-1]
                output_sequence = sentences[j]
            input_seq_sentence = ''
            output_seq_sentence = ''

            for word_idx in input_sequence:
                input_seq_sentence += idx2word[word_idx] + " "
            for word_idx in output_sequence:
                output_seq_sentence += idx2word[word_idx] + " "

            print("input_seq_sentence: ", input_seq_sentence)
            print("output_seq_sentence: ", output_seq_sentence)
            print("n_total: ", n_total)
            # has to be calculated manually
            n_total += len(output_sequence)
            keypressed = input('Press q to quit: ')
            if keypressed == 'q':
                break
        keypressed = input('Press q to quit: ')
        if keypressed == 'q':
            break

Example #17

File: srn_language_tf.py Project: Sebster10/Pandas-Practice

def train_poetry(session, dims, savefile):
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(dims, dims, len(word2idx), tf.nn.relu, session)
    rnn.fit(sentences, epochs=17, show_fig=True)
    rnn.save(savefile)

Example #18

def generate_potery():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load(
        'RRNN_D50_M50_epochs2000_relu_my.npz')  # sprawdzic czy dziala tez bez
    #rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu_my', T.nnet.relu)
    rnn.generate(word2idx)

Example #19

File: srn_language.py Project: cmagnusb/machine_learning_examples

def train_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(30, 30, len(word2idx))
    rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu, epochs=2000)
    rnn.save('RNN_D30_M30_epochs2000_relu.npz')

Example #20

def train_poetry():
    # students: tanh didn't work but you should try it
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(50, 50, len(word2idx))

Example #21

File: srn_language.py Project: CesarChaMal/machine_learning_examples

def train_poetry():
    # students: tanh didn't work but you should try it
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN(30, 30, len(word2idx))
    rnn.fit(sentences, learning_rate=10e-5, show_fig=True, activation=T.nnet.relu, epochs=2000)
    rnn.save('RNN_D30_M30_epochs2000_relu.npz')

Example #22

File: rrnn_language.py Project: CesarChaMal/machine_learning_examples

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RRNN_D50_M50_epochs2000_relu.npz', T.nnet.relu)
    rnn.generate(word2idx)

Example #23

File: srn_language.py Project: deepakrana47/Learned_codes

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load("RNN_D30_M30_epochs400_relu.npz",
                         activation=T.nnet.relu)
    rnn.generate(word2idx)

Example #24

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rnn = SimpleRNN.load('RRNN_D50_M50_epochs2000_relu.npz', T.nnet.relu)
    rnn.generate(word2idx)

Example #25

File: rrnn_language.py Project: guilhermevarela/machinelearning

def generate_poetry():
    sentences, word2idx = get_robert_frost()
    rrnn = RRNN.load('RRNN_D30_M30_epochs500_relu.npz', T.nnet.relu)

    rrnn.generate(word2idx)