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Functions1,905 in github.com/lazyprogrammer/machine_learning_examples

⨍ Functions 1,905◇ Types & classes 254↳ Endpoints 4
↓ 6 callersMethodtransform
(self, observations)
rl2/cartpole/q_learning.py:63
↓ 5 callersFunctiondense
(inputs, n, act=tf.nn.relu, gain=1.0)
rl3/a2c/neural_network.py:17
↓ 5 callersFunctiondot2
(H, W)
recommenders/rbm_tf_k.py:73
↓ 5 callersFunctiondownsample_image
(A)
rl2/atari/dqn_theano.py:44
↓ 5 callersMethodfit
(self, X, mask, X_test, mask_test, epochs=10, batch_sz=256, show_fig=True)
recommenders/rbm_tf_k.py:140
↓ 5 callersMethodforward
(self, X, reuse, is_training)
unsupervised_class3/dcgan_tf.py:57
↓ 5 callersMethodforward
(self, X, is_training)
unsupervised_class3/dcgan_theano.py:124
↓ 5 callersFunctionget_data
(split_sequences=False)
nlp_class2/pos_baseline.py:98
↓ 5 callersFunctionget_data
()
ann_logistic_extra/process.py:17
↓ 5 callersFunctionget_ptb_data
()
nlp_class2/util.py:161
↓ 5 callersFunctiongradW
(t, y, X)
ann_class2/util.py:182
↓ 5 callersFunctiongradb
(t, y)
ann_class2/util.py:186
↓ 5 callersFunctionmomentum_updates
(cost, params, mu, learning_rate)
unsupervised_class2/autoencoder.py:21
↓ 5 callersFunctionplot_cost_to_go
(env, estimator, num_tiles=20)
rl2/mountaincar/q_learning.py:127
↓ 5 callersFunctionplot_running_avg
(totalrewards)
rl2/mountaincar/q_learning.py:147
↓ 5 callersMethodsample
(self, n)
unsupervised_class3/dcgan_tf.py:498
↓ 5 callersMethodsave
(self, fn)
nlp_class2/glove.py:233
↓ 5 callersMethodtrain
(states, rewards, actions, values)
rl3/a2c/a2c.py:72
↓ 4 callersMethod_decision_function
(self, X)
svm_class/linear_svm_gradient.py:60
↓ 4 callersMethod_loss
(self, X, Y)
svm_class/svm_smo.py:43
↓ 4 callersFunctionall_parity_pairs_with_sequence_labels
(nbit)
rnn_class/util.py:38
↓ 4 callersFunctionbatch_norm
( input_, gamma, beta, running_mean, running_var, is_training, axes='per-activation')
unsupervised_class3/dcgan_theano.py:62
↓ 4 callersFunctionconvpool
(X, W, b, poolsize=(2, 2))
cnn_class/cnn_theano.py:28
↓ 4 callersFunctiondot1
(V, W)
recommenders/rbm_tf_k_faster.py:21
↓ 4 callersFunctionentropy
(y)
supervised_class/dt.py:16
↓ 4 callersFunctionf
(x, y)
rl3v2/visualize_es.py:5
↓ 4 callersMethodf1_score
(self, X, Y)
nlp_class2/pos_rnn.py:142
↓ 4 callersMethodf1_score
(self, X, Y)
nlp_class2/pos_baseline.py:93
↓ 4 callersFunctionfind_analogies
(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json')
rnn_class/batch_wiki.py:150
↓ 4 callersFunctionfind_analogies
(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json')
rnn_class/wiki.py:155
↓ 4 callersMethodfit
(self, df)
supervised_class2/rf_regression.py:44
↓ 4 callersMethodforward
(self, X)
unsupervised_class3/vae_theano.py:24
↓ 4 callersMethodforward
(self, X)
unsupervised_class3/vae_tf.py:36
↓ 4 callersMethodforward
(self, X)
rl2/mountaincar/pg_theano.py:57
↓ 4 callersMethodforward
(self, X)
rl2/mountaincar/pg_tf.py:41
↓ 4 callersFunctiongetKaggleMNIST
()
keras_examples/util.py:13
↓ 4 callersFunctiongetKaggleMNIST
()
svm_class/util.py:16
↓ 4 callersFunctionget_bigram_probs
(sentences, V, start_idx, end_idx, smoothing=1)
nlp_class2/markov.py:19
↓ 4 callersFunctionget_clouds
()
svm_class/util.py:105
↓ 4 callersFunctioninit_filter
(shape, poolsz)
cnn_class/cnn_theano.py:46
↓ 4 callersFunctioninit_weights
(shape)
ann_class/tf_example.py:41
↓ 4 callersMethodis_empty
(self, i, j)
rl/tic_tac_toe.py:144
↓ 4 callersMethodload
(self, name)
pytorch/rl_trader.py:345
↓ 4 callersFunctionmake_poly
(X, deg)
linear_regression_class/overfitting.py:15
↓ 4 callersFunctionmaybe_download
Download the data from Yann's website, unless it's already here.
tensorflow/input_data.py:11
↓ 4 callersFunctionmse
(p, t)
recommenders/userbased.py:159
↓ 4 callersMethodoutput
(self, x)
rnn_class/gru.py:54
↓ 4 callersMethodpartial_fit
(self, X, Y)
rl2/cartpole/pg_theano.py:166
↓ 4 callersFunctionplot_confusion_matrix
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`.
cnn_class2/util.py:14
↓ 4 callersFunctionplot_decision_boundary
(X, model)
supervised_class2/knn_dt_demo.py:95
↓ 4 callersMethodpredict
(self, X)
rl/linear_rl_trader.py:66
↓ 4 callersMethodpredict
(self, X)
supervised_class/knn.py:30
↓ 4 callersFunctionrearrange
(X)
cnn_class/cnn_theano.py:51
↓ 4 callersMethodscore
(self, X, Y)
ann_class2/batch_norm_theano.py:203
↓ 4 callersMethodscore
(self, X, Y)
ann_class2/batch_norm_tf.py:175
↓ 4 callersMethodscore
(self, X, Y)
ann_class2/tf_with_save.py:108
↓ 4 callersMethodscore
(self, X, Y)
supervised_class/perceptron.py:66
↓ 4 callersMethodscore
(self, X, Y)
supervised_class/dt_without_recursion.py:250
↓ 4 callersMethodstep
(self, action)
pytorch/rl_trader.py:199
↓ 4 callersFunctionto_bin
(value, bins)
rl2/cartpole/q_learning_bins.py:29
↓ 4 callersFunctiontrain
(env_id, num_timesteps, num_cpu)
rl3/a2c/main.py:33
↓ 4 callersMethodupdate
(self, s, a, G, gamma, lambda_)
rl2/mountaincar/td_lambda.py:63
↓ 4 callersMethodupdate
(self, x)
rl/ucb1.py:29
↓ 3 callersFunctionANN
(x, layer_sizes, hidden_activation=tf.nn.relu, output_activation=None)
rl3/ddpg.py:25
↓ 3 callersFunctionVGG16_AvgPool
(shape)
cnn_class2/style_transfer1.py:32
↓ 3 callersMethod_decision_function
(self, X)
svm_class/svm_smo.py:248
↓ 3 callersMethod_take_step
(self, i1, i2)
svm_class/svm_smo.py:49
↓ 3 callersMethodact
(self, state)
rl/linear_rl_trader.py:263
↓ 3 callersFunctionadd2dict
(d, k, v)
hmm_class/frost.py:35
↓ 3 callersFunctionall_parity_pairs
(nbit)
rnn_class/util.py:20
↓ 3 callersFunctionbig_init
()
hmm_class/generate_c.py:26
↓ 3 callersFunctionconv
(inputs, nf, ks, strides, gain=1.0)
rl3/a2c/neural_network.py:11
↓ 3 callersFunctiond
(u, v)
unsupervised_class/kmeans_visualize.py:14
↓ 3 callersFunctiondist
(a, b)
unsupervised_class/evolution.py:25
↓ 3 callersMethoddraw_board
(self)
rl/tic_tac_toe.py:234
↓ 3 callersFunctionembed_and_sum
(x, axis=2)
nlp_class3/memory_network.py:329
↓ 3 callersFunctionentropy
(y)
supervised_class/dt_without_recursion.py:16
↓ 3 callersFunctionepsilon_greedy
(model, s, eps=0.1)
rl/cartpole_gym0.19.py:18
↓ 3 callersFunctionepsilon_greedy
(model, s, eps=0.1)
rl/cartpole.py:18
↓ 3 callersFunctionerror_rate
(p, t)
unsupervised_class2/util.py:17
↓ 3 callersFunctionf
(x, y)
rl3v2/visualize_hill_climbing.py:5
↓ 3 callersMethodfit
(self, X, max_iter=30, eps=1e0)
hmm_class/hmmc.py:28
↓ 3 callersMethodfit
(self, X, Y, V=None, K=None, D=50, lr=1e-1, mu=0.99, batch_sz=100, epochs=6)
nlp_class2/pos_baseline.py:28
↓ 3 callersMethodfit
(self, X, Y, M=None)
supervised_class2/rf_vs_bag2.py:40
↓ 3 callersMethodfit
(self, X)
unsupervised_class3/dcgan_tf.py:407
↓ 3 callersMethodfit
(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=False)
unsupervised_class2/xwing.py:45
↓ 3 callersMethodfit
(self, X, Y, Xtest, Ytest, pretrain=True, train_head_only=False, learning_rate=0.1,
unsupervised_class2/autoencoder.py:142
↓ 3 callersMethodfit
(self, X, Y, learning_rate=1.0, epochs=1000)
supervised_class/perceptron.py:31
↓ 3 callersFunctionforward
(X, W1, b1, W2, b2)
ann_class/xor_donut.py:19
↓ 3 callersMethodforward_hidden
(self, X)
unsupervised_class2/autoencoder_tf.py:82
↓ 3 callersMethodgame_over
(self, force_recalculate=False)
rl/tic_tac_toe.py:176
↓ 3 callersMethodgenerate
(self, pi, word2idx)
rnn_class/srn_language.py:182
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmd_tf.py:53
↓ 3 callersMethodget_cost_multi
(self, X, p_cost=1.0)
hmm_class/hmmd_theano.py:98
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmd_theano2.py:89
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmc_tf.py:167
↓ 3 callersFunctionget_data
()
supervised_class2/rf_classification.py:87
↓ 3 callersFunctionget_r2
(X, Y)
linear_regression_class/systolic.py:41
↓ 3 callersFunctionget_score
(sentence)
nlp_class2/markov.py:75
↓ 3 callersFunctionget_simple_data
()
unsupervised_class/kmeans.py:92
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