def test_computational_graph3(self): # validate the number of updates found by ComputationGraph X = K.placeholder(shape=(None, 28, 28, 3)) f = N.Sequence([ N.Conv(32, 3, pad='same', activation=K.linear), N.BatchNorm(activation=K.relu), N.Flatten(outdim=2), N.Dense(16), N.BatchNorm(), N.Dense(10) ]) K.set_training(True) y_train = f(X) K.set_training(False) y_score = f(X) self.assertTrue( K.get_shape(y_train) == K.get_shape(y_score) and K.get_shape(y_score) == (None, 10)) cc_train = K.ComputationGraph(y_train) cc_score = K.ComputationGraph(y_score) self.assertTrue(len(cc_score.updates) == 0) self.assertTrue(len(cc_train.updates) == 4) # create real function fn_train = K.function(X, y_train) fn_score = K.function(X, y_score) shape1 = fn_train(np.random.rand(12, 28, 28, 3)).shape shape2 = fn_score(np.random.rand(12, 28, 28, 3)).shape self.assertTrue(shape1 == shape2 and shape1 == (12, 10))
def gender(X, f, **kwargs): nb_gender = kwargs.get('nb_gender', 4) if f is None: f = N.Sequence([ N.Dimshuffle(pattern=(0, 1, 2, 'x')), N.Conv(num_filters=32, filter_size=3, strides=1, b_init=None, pad='valid'), N.BatchNorm(activation=K.relu), N.Pool(pool_size=2, mode='avg'), N.Conv(num_filters=64, filter_size=3, strides=1, b_init=None, pad='valid'), N.BatchNorm(activation=K.relu), N.Pool(pool_size=2, mode='avg'), N.Flatten(outdim=3), N.Dense(num_units=512, b_init=None), N.BatchNorm(axes=(0, 1)), N.AutoRNN(num_units=128, rnn_mode='gru', num_layers=2, input_mode='linear', direction_mode='unidirectional'), N.Flatten(outdim=2), N.Dense(num_units=nb_gender, activation=K.softmax) ], debug=True) return f(X), f
def cnn(X, y): nb_classes = y.shape.as_list()[-1] with N.args_scope(['Conv', dict(b_init=None, activation=K.linear)], ['BatchNorm', dict(activation=K.relu)]): f = N.Sequence([ N.Dimshuffle(pattern=(0, 2, 3, 1)), N.Conv(32, (3, 3), pad='same', stride=(1, 1)), N.BatchNorm(), N.Conv(32, (3, 3), pad='same', stride=(1, 1), b_init=0, activation=K.relu), N.Pool(pool_size=(2, 2), strides=None, mode='max'), N.Dropout(level=0.25), # N.Conv(64, (3, 3), pad='same', stride=(1, 1)), N.BatchNorm(), N.Conv(64, (3, 3), pad='same', stride=(1, 1), b_init=0., activation=K.relu), N.Pool(pool_size=(2, 2), strides=None, mode='max'), N.Dropout(level=0.25), # N.Flatten(outdim=2), N.Dense(512, activation=K.relu), N.Dropout(level=0.5), N.Dense(nb_classes, activation=K.linear) ], debug=1) logit = f(X) prob = tf.nn.softmax(logit) return {'logit': logit, 'prob': prob}
def test_batch_norm(self): K.set_training(True) x = K.placeholder((None, 8, 12)) y = N.BatchNorm()(x) f = K.function(x, y) z = f(np.random.rand(25, 8, 12)) self.assertEquals(z.shape, (25, 8, 12)) # ====== Not training ====== # K.set_training(False) x = K.placeholder((None, 8, 12)) y = N.BatchNorm()(x) f = K.function(x, y) z = f(np.random.rand(25, 8, 12)) self.assertEquals(z.shape, (25, 8, 12))
def dense_creator(): net = N.Sequence([ N.Dense(int(args.hdim), b_init=0 if args.no_batchnorm else None, activation=K.relu if args.no_batchnorm else K.linear), None if args.no_batchnorm else N.BatchNorm(activation=K.relu) ], debug=True, name="DenseBatchNorm%d" % index[0]) index[0] += 1 return net
def test_helper_ops_variables(self): X = K.placeholder(shape=(10, 20)) f = N.Sequence([ N.Dense(12), N.Dense(8), N.BatchNorm(), N.Dense(25, W_init=tf.zeros(shape=(8, 25))) ]) y = f(X) self.assertEqual(y.shape.as_list(), [10, 25]) self.assertEqual(len(f.variables), 10) self.assertEqual(len(f.parameters), 7) self.assertEqual(len(f.trainable_variables), 9)
def test_slice_ops(self): X = K.placeholder(shape=(None, 28, 28, 28, 3)) f = N.Sequence([ N.Conv(32, 3, pad='same', activation=K.linear), N.BatchNorm(activation=tf.nn.relu), N.Flatten(outdim=4)[:, 8:12, 18:25, 13:], ]) y = f(X) fn = K.function(X, y) self.assertTrue( fn(np.random.rand(12, 28, 28, 28, 3)).shape[1:] == tuple( y.shape.as_list()[1:])) self.assertEqual(y.shape.as_list()[1:], [4, 7, 883])
def ladder1(X, y, states, **kwargs): noise = kwargs.get('noise', 0.3) # hyperparameters that denote the importance of each layer denoising_cost = [1000.0, 10.0, 0.10, 0.10, 0.10] if states is None: # f_encoder = N.Sequence([ N.Flatten(outdim=2), N.Dense(num_units=1024, b_init=None), N.BatchNorm( axes=0, noise_level=noise, noise_dims=None, activation=K.relu), N.Dense(num_units=512, b_init=None), N.BatchNorm( axes=0, noise_level=noise, noise_dims=None, activation=K.relu), N.Dense(num_units=256, b_init=None), N.BatchNorm( axes=0, noise_level=noise, noise_dims=None, activation=K.relu), N.Dense(num_units=128, b_init=None), N.BatchNorm( axes=0, noise_level=noise, noise_dims=None, activation=K.relu), N.Dense(num_units=10, activation=K.softmax), ], all_layers=True, debug=True, name='Encoder') # f_decoder = N.Sequence([ N.Dense(num_units=128, b_init=None), N.BatchNorm(axes=0, activation=K.relu), N.Dense(num_units=256, b_init=None), N.BatchNorm(axes=0, activation=K.relu), N.Dense(num_units=512, b_init=None), N.BatchNorm(axes=0, activation=K.relu), N.Dense(num_units=1024, b_init=None), N.BatchNorm(axes=0, activation=K.relu), N.Reshape(shape=(-1, 28, 28)), ], all_layers=True, debug=True, name='Decoder') else: f_encoder, f_decoder = states y_encoder_clean = f_encoder(X, noise=-1)[2::2] y_encoder_corrp = f_encoder(X, noise=1)[2::2] print(len(y_encoder_clean), len(y_encoder_corrp)) exit() return (None, None), [f_encoder, f_decoder]
X = inputs[0] y = inputs[1] print("Inputs:", ctext(inputs, 'cyan')) # ====== create the networks ====== # with N.args_scope( ['TimeDelayedConv', dict(time_pool='none', activation=K.relu)], ['Dense', dict(activation=K.linear, b_init=None)]): f = N.Sequence([ N.Dropout(level=0.3), N.TimeDelayedConv(n_new_features=512, n_time_context=5), N.TimeDelayedConv(n_new_features=512, n_time_context=5), N.TimeDelayedConv( n_new_features=512, n_time_context=7, name="LatentTDNN"), N.Dense(512), N.BatchNorm(activation=K.relu), N.Dense(1500), N.BatchNorm(activation=K.relu), N.StatsPool(axes=1, output_mode='concat'), N.Flatten(outdim=2, name="StatsPooling"), N.Dense(512, name="LatentDense"), N.BatchNorm(activation=K.relu), N.Dense(512), N.BatchNorm(activation=K.relu), N.Dense(num_units=n_classes, activation=K.linear, b_init=init_ops.constant_initializer(0)) ], debug=1) # ====== create outputs ====== # y_logit = f(X)
USE_MNIST_DATA = True if 'mnist' in arg['ds'].lower() else False if USE_MNIST_DATA: ds = fuel.load_mnist() else: ds = fuel.load_cifar10() X = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:], name='X') y = K.placeholder(shape=(None, ), name='y', dtype='int32') # =========================================================================== # Build network # =========================================================================== ops = N.Sequence([ N.Dimshuffle((0, 1, 2, 'x')) if USE_MNIST_DATA else None, N.BatchNorm(axes='auto'), N.Conv(32, (3, 3), strides=(1, 1), pad='same', activation=K.relu), N.Pool(pool_size=(2, 2), strides=None), N.Conv(64, (3, 3), strides=(1, 1), pad='same', activation=K.relu), N.Pool(pool_size=(2, 2), strides=None), N.Flatten(outdim=2), N.Dense(256, activation=K.relu), N.Dense(10, activation=K.softmax) ], debug=True) ops = cPickle.loads(cPickle.dumps(ops)) # test if the ops is pickle-able K.set_training(True) y_pred_train = ops(X) K.set_training(False) y_pred_score = ops(X)
x_vec = N.deserialize(path=all_models[SCORE_SYSTEM_ID], force_restore_vars=True) else: with N.args_scope( ['TimeDelayedConv', dict(time_pool='none', activation=K.relu)], ['Dense', dict(activation=K.linear, b_init=None)], ['BatchNorm', dict(activation=K.relu)] ): x_vec = N.Sequence([ N.Dropout(level=0.3), N.TimeDelayedConv(n_new_features=512, n_time_context=5), N.TimeDelayedConv(n_new_features=512, n_time_context=5), N.TimeDelayedConv(n_new_features=512, n_time_context=7), N.Dense(512), N.BatchNorm(), N.Dense(1500), N.BatchNorm(), N.StatsPool(axes=1, output_mode='concat'), N.Flatten(outdim=2), N.Dense(512, name="LatentOutput"), N.BatchNorm(), N.Dense(512), N.BatchNorm(), N.Dense(n_speakers, activation=K.linear, b_init=init_ops.constant_initializer(value=0)) ], debug=1, name='XNetwork') # ====== create outputs ====== # y_logit = x_vec(X) y_proba = tf.nn.softmax(y_logit) z = K.ComputationGraph(y_proba).get(roles=N.Dense, scope='LatentOutput',
def convolutional_vae(X, saved_states, **kwargs): """ convolutional_vae Return ------ [y_encoder, y_decoder] States ------ [f_inference (encoder), f_generative (decoder)] """ n = kwargs.get('n', 10) batch_size = K.get_shape(X)[0] if batch_size is None: raise ValueError( "You must specify batch_size dimension for the input placeholder.") # ====== init ====== # if saved_states is None: # Encoder f_inference = N.Sequence([ N.Reshape(shape=(-1, 28, 28, 1)), N.Conv(num_filters=32, filter_size=3, strides=1, pad='valid', b_init=init_ops.constant_initializer(0.), activation=K.elu), N.Conv(num_filters=64, filter_size=5, strides=2, pad='same', b_init=init_ops.constant_initializer(0.), activation=K.elu), N.Dropout(level=0.1), N.Flatten(outdim=2), N.Dense(num_units=n * 2, b_init=None), N.BatchNorm(axes=0) ], debug=True, name='Encoder') # Decoder f_generative = N.Sequence([ N.Dimshuffle(pattern=(0, 'x', 'x', 1)), N.TransposeConv(num_filters=64, filter_size=3, strides=1, pad='valid', b_init=init_ops.constant_initializer(0.), activation=K.elu), N.TransposeConv(num_filters=32, filter_size=5, strides=2, pad='same', b_init=init_ops.constant_initializer(0.), activation=K.elu), N.TransposeConv(num_filters=1, filter_size=13, strides=3, pad='valid', b_init=None), N.BatchNorm(activation=K.linear), N.Flatten(outdim=3) ], debug=True, name="Decoder") else: f_inference, f_generative = saved_states # ====== Perfrom ====== # # Encoder y_encoder = f_inference(K.cast(X, 'float32')) mu = y_encoder[:, :n] sigma = K.softplus(y_encoder[:, n:]) qz = Normal(mu=mu, sigma=sigma, name='Normal_qz') # Decoder z = Normal(mu=K.zeros(shape=(batch_size, n)), sigma=K.ones(shape=(batch_size, n)), name="Normal_pz") logits = f_generative(z) X_reconstruct = Bernoulli(logits=logits) # inference params = f_inference.parameters + f_generative.parameters inference = ed.KLqp(latent_vars={z: qz}, data={X_reconstruct: X}) # ====== get cost for training ====== # # Bind p(x, z) and q(z | x) to the same placeholder for x. if K.is_training(): import tensorflow as tf inference.initialize() if True: optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0) updates = optimizer.apply_gradients( optimizer.compute_gradients(inference.loss, var_list=params)) init = tf.global_variables_initializer() init.run() f_train = K.function(X, inference.loss, updates) else: optimizer = tf.train.AdamOptimizer(0.01, epsilon=1.0) inference.initialize(optimizer=optimizer, var_list=params) init = tf.global_variables_initializer() init.run() f_train = lambda x: inference.update(feed_dict={X: x})['loss'] samples = K.sigmoid(logits) return (samples, z, qz), (f_inference, f_generative)
test.set_recipes(recipes) # =========================================================================== # Create model # =========================================================================== inputs = [ K.placeholder(shape=(None, ) + shape[1:], dtype='float32', name='input%d' % i) for i, shape in enumerate(train.shape) ] print("Inputs:", ctext(inputs, 'cyan')) # ====== create the network ====== # f_encoder = N.Sequence([ N.Dimshuffle(pattern=(0, 1, 2, 'x')), N.Conv( num_filters=32, filter_size=(7, 7), b_init=None, activation=K.linear), N.BatchNorm(), N.Pool(pool_size=(3, 2), strides=2), ], debug=True, name='Encoder') f_latent = N.Sequence([ N.Flatten(outdim=3), N.CudnnRNN( num_units=128, num_layers=1, is_bidirectional=False, rnn_mode='lstm'), ], debug=True, name='Latent') f_decoder = N.Sequence([ N.Flatten(outdim=2), N.Dense(num_units=1024, b_init=None, activation=K.linear), N.BatchNorm(axes=0, activation=K.relu)
ds = fuel.load_mnist() else: ds = fuel.load_cifar10() print(ds) X = K.placeholder(shape=(None, ) + ds['X_train'].shape[1:], name='X') y = K.placeholder(shape=(None, ), name='y', dtype='int32') # =========================================================================== # Build network # =========================================================================== ops = N.Sequence([ N.Dimshuffle((0, 1, 2, 'x')) if USE_MNIST_DATA else N.Dimshuffle( (0, 2, 3, 1)), N.Conv(32, filter_size=3, strides=1, pad='same', activation=K.linear), N.BatchNorm(axes='auto', activation=K.relu), N.Pool(pool_size=2, strides=None), N.Dimshuffle(pattern=(0, 3, 1, 2)), N.Flatten(outdim=3), N.CudnnRNN(18, initial_states=None, rnn_mode='lstm', num_layers=2, input_mode='linear', direction_mode='unidirectional', params_split=False), N.Flatten(outdim=2), N.Dense(128, activation=K.relu), N.Dense(10, activation=K.softmax) ], debug=True)
K.placeholder(shape=(None, ) + shape[1:], dtype='float32', name='input%d' % i) for i, shape in enumerate(as_tuple_of_shape(train.shape)) ] X = inputs[0] y = inputs[1] print("Inputs:", ctext(inputs, 'cyan')) # ====== create the networks ====== # with N.args_scope([('Conv', 'Dense'), dict(b_init=None, activation=K.linear, pad='same')], ['BatchNorm', dict(activation=K.relu)]): f = N.Sequence([ N.Dimshuffle(pattern=(0, 1, 2, 'x')), N.Conv(num_filters=32, filter_size=(9, 7)), N.BatchNorm(), N.Pool(pool_size=(3, 2), strides=2), N.Conv(num_filters=64, filter_size=(5, 3)), N.BatchNorm(), N.Pool(pool_size=(3, 1), strides=(2, 1), name='PoolOutput1'), N.Conv(num_filters=64, filter_size=(5, 3)), N.BatchNorm(), N.Pool(pool_size=(3, 2), strides=(2, 2), name='PoolOutput2'), N.Flatten(outdim=2), N.Dense(512, name="LatentDense"), N.BatchNorm(), N.Dense(512), N.BatchNorm(), N.Dense(n_classes) ], debug=1)
y = K.placeholder(shape=(None,), name='y_input') # =========================================================================== # Create the network # =========================================================================== LATENT_DROPOUT = 0.3 if args.cnn: with N.args_scope(([N.Conv, N.Dense], dict(b_init=None, activation=K.linear)), (N.BatchNorm, dict(activation=tf.nn.elu)), (N.Pool, dict(mode='max', pool_size=2))): f_encoder = N.Sequence([ N.Dropout(level=0.5), N.Dimshuffle((0, 2, 3, 1)) if is_cifar10 else N.Dimshuffle((0, 1, 2, 'x')), N.Conv(num_filters=32, filter_size=3, pad='valid'), N.Pool(), N.BatchNorm(), N.Conv(num_filters=64, filter_size=3, pad='same'), N.BatchNorm(), N.Conv(num_filters=64, filter_size=3, pad='valid'), N.BatchNorm(activation=tf.nn.elu), N.Pool(), N.Flatten(outdim=2), N.Dense(num_units=args.dim) ], debug=True, name='EncoderNetwork') f_decoder = N.Sequence([ N.Dropout(level=LATENT_DROPOUT, noise_type='uniform'), N.Noise(level=1.0, noise_type='gaussian'),
# =========================================================================== # Create model # =========================================================================== inputs = [K.placeholder(shape=(None,) + shape[1:], dtype='float32', name='input%d' % i) for i, shape in enumerate(as_tuple_of_shape(train.shape))] X = inputs[0] y = inputs[1] print("Inputs:", ctext(inputs, 'cyan')) # ====== create the networks ====== # with N.args_scope( [('Conv', 'Dense'), dict(b_init=None, activation=K.linear, pad='same')], ['BatchNorm', dict(activation=K.relu)]): f = N.Sequence([ N.Dimshuffle(pattern=(0, 1, 2, 'x')), N.Conv(num_filters=32, filter_size=(9, 7)), N.BatchNorm(), N.Pool(pool_size=(3, 2), strides=2), N.Conv(num_filters=64, filter_size=(5, 3)), N.BatchNorm(), N.Pool(pool_size=(3, 1), strides=(2, 1), name='PoolOutput1'), N.Conv(num_filters=64, filter_size=(5, 3)), N.BatchNorm(), N.Pool(pool_size=(3, 2), strides=(2, 2), name='PoolOutput2'), N.Flatten(outdim=2), N.Dense(512, name="LatentDense"), N.BatchNorm(), N.Dense(512), N.BatchNorm(), N.Dense(n_classes) ], debug=1) # ====== create outputs ====== # y_logit = f(X)