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def test_feed_dict_no_None(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4], name="X_in")
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2)
g = tflearn.conv_2d(g, 4, 1)
g = tflearn.max_pool_2d(g, 2)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
def do_fit():
m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False)
self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
gen_loss = -tf.reduce_mean(tf.log(disc_fake))
# Build Training Ops for both Generator and Discriminator.
# Each network optimization should only update its own variable, thus we need
# to retrieve each network variables (with get_layer_variables_by_scope) and set
# 'placeholder=None' because we do not need to feed any target.
gen_vars = tflearn.get_layer_variables_by_scope('Generator')
gen_model = tflearn.regression(gen_sample, placeholder=None, optimizer='adam',
loss=gen_loss, trainable_vars=gen_vars,
batch_size=64, name='target_gen', op_name='GEN')
disc_vars = tflearn.get_layer_variables_by_scope('Discriminator')
disc_model = tflearn.regression(disc_real, placeholder=None, optimizer='adam',
loss=disc_loss, trainable_vars=disc_vars,
batch_size=64, name='target_disc', op_name='DISC')
# Define GAN model, that output the generated images.
gan = tflearn.DNN(gen_model)
# Training
# Generate noise to feed to the generator
z = np.random.uniform(-1., 1., size=[total_samples, z_dim])
# Start training, feed both noise and real images.
gan.fit(X_inputs={gen_input: z, disc_input: X},
Y_targets=None,
n_epoch=100)
# Generate images from noise, using the generator network.
f, a = plt.subplots(2, 10, figsize=(10, 4))
for i in range(10):
for j in range(2):
# Noise input.
z = np.random.uniform(-1., 1., size=[1, z_dim])
# Generate image from noise. Extend to 3 channels for matplot figure.
def train_network(net, epochs, train, valid, asset):
"""
Run training for epochs iterations
train: tuple of (data, target)
valid: tuple of (data, target)
"""
# declare model
model = tfl.DNN(net, tensorboard_dir="./logs_tb", tensorboard_verbose=3)
# Train model
model.fit({'input': train[0]}, {'target': train[1]}, n_epoch=epochs,
validation_set=({'input': valid[0]}, {'target': valid[1]}),
show_metric=True, shuffle=False)
directory = "networks/" + asset
if not os.path.exists(directory):
os.makedirs(directory)
model.save(directory + "/lstm3.tflearn")
return model
# Building convolutional network
network = input_data(shape=[None, 100], name='input')
network = tflearn.embedding(network, input_dim=10000, output_dim=128)
branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
network = merge([branch1, branch2, branch3], mode='concat', axis=1)
network = tf.expand_dims(network, 2)
network = global_max_pool(network)
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY, n_epoch = 5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
drug_reshape = tflearn.reshape(drug_data, [-1, 128,2])
conv_3 = conv_1d(drug_reshape, 64, 4,2, activation='leakyrelu', weights_init="xavier",regularizer="L2",name='conv3')
pool_3 = max_pool_1d(conv_3, 4,name='pool3')
drug_reshape_4 = tflearn.reshape(pool_3, [-1, 64*16])
merging = merge([prot_reshape_4,drug_reshape_4],mode='concat',axis=1)
fc_1 = fully_connected(merging, 300, activation='leakyrelu',weights_init="xavier",name='fully1')
drop_2 = dropout(fc_1, 0.8)
fc_2 = fully_connected(drop_2, 100, activation='leakyrelu',weights_init="xavier",name='fully2')
drop_3 = dropout(fc_2, 0.8)
linear = fully_connected(drop_3, 1, activation='linear',name='fully3')
reg = regression(linear, optimizer='adam', learning_rate=0.001,
loss='mean_square', name='target')
# Training
model = tflearn.DNN(reg, tensorboard_verbose=0,tensorboard_dir='./mytensor/',checkpoint_path="./checkpoints/")
######## training
model.fit([protein_train,compound_train], {'target': kd_train}, n_epoch=100,batch_size=batch_size,
validation_set=([protein_dev,compound_dev], {'target': kd_dev}),
show_metric=True, run_id='joint_model')
# saving model
model.save('my_model.tflearn')
print("error on ER")
size = 5000
num_bins = math.ceil(length_ER/size)
for i in range(num_bins):
if i==0:
y_pred = model.predict([feature_ER_protein[0:size,],feature_ER_compound[0:size,]])
elif i < num_bins-1:
pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
prev_model = '../model/tflearn/reg_l3_bn/big/%s' % pred_s_date
self.model_dir = '../model/tflearn/reg_l3_bn/big/%s' % s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.1)
input_layer = tflearn.input_data(shape=[None, 23*n_frame], name='input')
dense1 = tflearn.fully_connected(input_layer, 400, name='dense1', activation='relu')
dense1n = tflearn.batch_normalization(dense1, name='BN1')
dense2 = tflearn.fully_connected(dense1n, 100, name='dense2', activation='relu')
dense2n = tflearn.batch_normalization(dense2, name='BN2')
dense3 = tflearn.fully_connected(dense2n, 1, name='dense3')
output = tflearn.single_unit(dense3)
regression = tflearn.regression(output, optimizer='adam', loss='mean_square',
metric='R2', learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
if os.path.exists('%s/model.tfl' % prev_model):
self.estimators.load('%s/model.tfl' % prev_model)
self.n_epoch = 10
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
def model(train_x,train_y):
'''Model Definition of a Deep Neural Network, which is responsible for the Classification.
Input matrix size is set dynamically.
'''
net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
return model
def sentnet_LSTM_gray(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height], name='input')
#network = tflearn.input_data(shape=[None, 28, 28], name='input')
network = tflearn.lstm(network, 128, return_seq=True)
network = tflearn.lstm(network, 128)
network = tflearn.fully_connected(network, 9, activation='softmax')
network = tflearn.regression(network, optimizer='adam',
loss='categorical_crossentropy', name="output1")
model = tflearn.DNN(network, checkpoint_path='model_lstm',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
inception_7a_output = merge([inception_7a_1_1, inception_7a_3_3, inception_7a_5_5, inception_7a_pool_1_1], mode='concat', axis=3)
pool5_7_7=global_avg_pool(inception_7a_output)
pool5_7_7=dropout(pool5_7_7,0.4)
loss = fully_connected(pool5_7_7, 2,activation='softmax')
if(training):
network = regression(loss, optimizer='rmsprop',
loss='categorical_crossentropy',
learning_rate=0.001)
else:
network=loss
model = tflearn.DNN(network, checkpoint_path='inceptionv3',
max_checkpoints=1, tensorboard_verbose=0)
return model
def __init__(self):
network = tflearn.input_data(shape=[None, 784], name="input")
network = self.make_core_network(network)
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=0)
self.model = model