def trainNNModel(self, X_train, y_train):
"""
Trains a four-layer tensorflow neural network: X->LINEAR->RELU->LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
Args:
X_train -- training set data
y_train -- training set labels
Returns:
cost -- Final cost after training
"""
# ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
tf.set_random_seed(1)
costs = []
load = DataLoader()
(n_x,m) = X_train.shape
n_y = y_train.shape[0]
# X,y = self.create_placeholders(n_x,n_y)
y_pred,_ = self.forward_pass()
cost = self.cross_entropy_loss(y_pred)
# cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits = tf.transpose(y_pred), labels = tf.transpose(self.y)))
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(cost)
seed = 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
for epoch in range(self.epochs):
epoch_cost = 0.
n_mb = int(m/self.mbatchsz)
seed += 1
minibatches = load.create_batches(X_train,y_train,n_mb,seed,self.mbatchsz)
for minibatch in minibatches:
mbX,mby = minibatch
_,mb_Cost = sess.run([optimizer,cost], feed_dict = {self.X: mbX, self.y: mby})
epoch_cost += mb_Cost/n_mb
print "Cost after epoch %i: %f" % (epoch, epoch_cost)
costs.append(epoch_cost)
finalCost = costs[-1]
yp,_ = self.forward_pass()
count = tf.equal(tf.argmax(yp), tf.argmax(self.y))
accuracy = tf.reduce_mean(tf.cast(count,"float"))
trainAcc = accuracy.eval({self.X: X_train, self.y: y_train})
print "Final training cost after epoch %i: %f"%(self.epochs,finalCost)
print "Train Accuracy for 4 layer neural network: ", trainAcc
savepath = saver.save(sess,"./weights/weights.cpkt")
return costs
def train_model_gru(n_neurons,
n_steps=28,
n_inputs=28,
n_outputs=10,
learning_rate=0.0001,
n_epochs=100,
batch_size=32):
X, y = create_placeholders(n_inputs, n_outputs, n_steps)
# lstm-cell
current_input = tf.unstack(X, n_steps, 1)
gru_cell_train = GRUCell_train(n_neurons, n_inputs)
outputs, states = tf.nn.static_rnn(gru_cell_train,
current_input,
dtype=tf.float32)
# fully-connected layer
FC_W = tf.get_variable(
"FC_W", [n_neurons, n_outputs],
initializer=tf.contrib.layers.xavier_initializer(seed=42))
FC_b = tf.get_variable("FC_b", [n_outputs],
initializer=tf.zeros_initializer())
Z = tf.add(tf.matmul(outputs[-1], FC_W), FC_b)
#optimizatio
loss = compute_loss(Z, y)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
correct_prediction = tf.equal(tf.argmax(Z, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
# data-loading
ld = DataLoader()
X_train, Y_train = ld.load_data()
Y_train = np.eye(10)[np.asarray(Y_train, dtype=np.int32)]
X_Batched, Y_Batched = ld.create_batches(X_train, Y_train, batch_size)
X_test, y_test = ld.load_data(mode='test')
y_test = np.eye(10)[np.asarray(y_test, dtype=np.int32)]
X_test = X_test.reshape((-1, n_steps, n_inputs))
saver = tf.train.Saver()
weight_filepath = "./weights/gru/hidden_unit" + str(
n_neurons) + "/model.ckpt"
with tf.Session() as sess:
init.run()
#training
for epoch in range(n_epochs):
for X_batch, y_batch in itertools.izip(X_Batched, Y_Batched):
X_batch = X_batch.reshape((-1, n_steps, n_inputs))
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
print("Train accuracy after %s epochs: %s" %
(str(epoch + 1), str(acc_train * 100)))
acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
print("Test accuracy: ", acc_test * 100)
# Save parameters in memory
saver.save(sess, weight_filepath)
return acc_test
def trainRNNmodel(self, X_train, y_train):
tf.set_random_seed(1)
costs = []
load = DataLoader()
m = X_train.shape[0]
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits,
labels=self.y))
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(self.pred, 1), tf.argmax(self.y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
seed = 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
for epoch in range(self.epochs):
epoch_cost = 0.
n_mb = int(m / self.mbatchsz)
seed += 1
minibatches = load.create_batches(X_train, y_train, n_mb, seed,
self.mbatchsz)
for minibatch in minibatches:
mbX, mby = minibatch
_, mb_Cost = sess.run([optimizer, cost],
feed_dict={
self.X: mbX,
self.y: mby
})
epoch_cost += mb_Cost / n_mb
print "Cost after epoch %i: %f" % (epoch, epoch_cost)
costs.append(epoch_cost)
finalCost, trainAcc = sess.run([cost, accuracy],
feed_dict={
self.X: mbX,
self.y: mby
})
print "Final training cost after epoch %i: %f" % (self.epochs,
finalCost)
print "Train Accuracy for", self.model, "recurrent neural network with %i units: " % (
self.n_h), trainAcc
savepath = saver.save(
sess,
"./weights/weights_" + self.model + str(self.n_h) + ".cpkt")
return costs
epoch_seed = 1
layers_dims = [784, 40, 10] # 2-layer model
learning_rate = 0.0001
regL2 = 0.0001
np.random.seed(100)
Net = NN(layers_dims, learning_rate, regL2)
costs = []
epochs = []
testaccs = []
for epoch in range(num_epochs):
epoch_seed += 1
epochcost = 0
minibatches = load.create_batches(X_train, y_train, num_batches,
epoch_seed, mbatch_sz)
for minibatch in minibatches:
mbX, mby = minibatch
y_pred, cache = Net.forward_pass(mbX)
# print y_pred
# print mbX.shape,mby.shape,y_pred.shape
epochcost += Net.cross_entropy_loss(mby, y_pred)
Net.backward_pass(mbX, mby, cache)
# if epoch%100 == 0:
epochs.append(epoch)
costs.append(epochcost / num_batches)
# argument parsing
parser = argparse.ArgumentParser()
parser.add_argument('--test' , action='store_true',help='To test the data')
parser.add_argument('--train', action='store_true',help='To train the data')
parser.add_argument('--hidden_unit', action='store',help='No of hidden units',type=int)
parser.add_argument('--model', action='store',help='which model will be used - LSTM or GRU')
args = parser.parse_args()
tf.set_random_seed(100)
np.random.seed(60)
no_hidden_units = args.hidden_unit
X = tf.placeholder(tf.float32, [None, vector_size, no_vectors])
Y = tf.placeholder(tf.int32, [None, no_classes])
# data-loading
data_loader = DataLoader()
x_train, y_train = data_loader.load_data()
y_train = np.eye(10)[np.asarray(y_train, dtype=np.int32)]
x_batch, y_batch = data_loader.create_batches(x_train, y_train, batch_size)
x_test,y_test = data_loader.load_data(mode='test')
y_test = np.eye(10)[np.asarray(y_test, dtype=np.int32)]
x_test = x_test.reshape((-1, vector_size, no_vectors))
if args.train:
_train_model(vector_size, no_vectors, no_classes, l_rate, epochs, batch_size)
elif args.test:
test_model(vector_size, no_vectors, no_classes)
def main():
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model_name = model_path + FLAGS.dataset
dataloader = DataLoader(BATCH_SIZE)
model = HACap(dataloader)
saver = tf.train.Saver(tf.global_variables())
sess.run(tf.global_variables_initializer())
if FLAGS.test:
saver.restore(sess, model_name)
dataloader.create_batches(file_path='./dataset/',
dataset_title=dataset_path[FLAGS.dataset],
batch_size=BATCH_SIZE,
data_type="train",
skip=0)
for i in range(dataloader.num_batches):
print('batches {} / {}'.format(i, dataloader.num_batches))
image_cap_tuple = dataloader.next_batch()
caption = model.generate_caption(sess,
image_cap_tuple,
is_realized=False)
image, _ = image_cap_tuple
image_cap_tuple = image, caption
draw_attention(
image_cap_tuple,
np.transpose(model.get_attention(sess, image_cap_tuple),
(1, 0, 2)), dataloader.idx2word, i)
'''
draw_guidance(image_cap_tuple,
np.transpose(model.get_guidance(sess, image_cap_tuple), (1, 2, 0)))
'''
'''
caption = model.generate_caption(sess, image_caption=image_cap_tuple)
image, _ = image_cap_tuple
handle_image_cap_tuple((image, caption))
'''
else:
dataloader.create_batches('./dataset/',
BATCH_SIZE,
dataset_path[FLAGS.dataset],
data_type="train",
skip=None)
print(dataloader.vocab_size)
if not FLAGS.init:
saver.restore(sess, model_name)
else:
if FLAGS.conti:
saver.restore(sess, model_name)
for pretrain_batch in range(10000):
if pretrain_batch < 5 and (not FLAGS.conti):
for pretrain_d_batch in range(5):
print("pretraining discriminator epoch#%d-%d" %
(pretrain_batch, pretrain_d_batch))
dataloader.reset_pointer()
for batch_idx in range(dataloader.num_batches):
if batch_idx % 1000 == 0:
print(
"pretraining discriminator epoch#%d-%d/%d"
% (pretrain_d_batch, batch_idx,
dataloader.num_batches))
batch = dataloader.next_batch()
if rdfloat() > skip_rate:
model.train_discriminator(sess, batch)
for pretrain_g_batch in range(15):
print("pretraining generator epoch#%d-%d" %
(pretrain_batch, pretrain_g_batch))
dataloader.reset_pointer()
for batch_idx in range(dataloader.num_batches):
batch = dataloader.next_batch()
if rdfloat() > skip_rate:
model.train_via_MLE(sess, batch)
if batch_idx % 1000 == 0:
print("pretraining generator epoch#%d-%d/%d" %
(pretrain_g_batch, batch_idx,
dataloader.num_batches))
image_cap_tuple = batch
caption = model.generate_caption(
sess,
image_caption=image_cap_tuple,
is_train=0.0)
image, truth = image_cap_tuple
print("fake:")
handle_image_cap_tuple((image, caption))
print("real:")
handle_image_cap_tuple(
(image, model.ind_to_str(truth)))
print("real_alpha:")
print(
model.get_attention(sess, image_cap_tuple)[0])
if FLAGS.save:
saver.save(sess, model_name)
for adv_batch in range(10000000):
print("adversarial training epoch#%d" % adv_batch)
dataloader.reset_pointer()
for batch_idx in range(dataloader.num_batches):
batch = dataloader.next_batch()
if rdfloat() > skip_rate:
for d_idx in range(2):
model.train_discriminator(sess, batch)
if adv_batch % 1 == 0:
for teacher_forcing_idx in range(5):
model.train_via_MLE(sess, batch)
if batch_idx % 1000 == 0:
image_cap_tuple = batch
print("adversarial training epoch#%d-%d/%d" %
(adv_batch, batch_idx,
dataloader.num_batches))
caption = model.generate_caption(
sess, image_caption=image_cap_tuple)
image, truth = image_cap_tuple
print("fake:")
handle_image_cap_tuple((image, caption))
print("real:")
handle_image_cap_tuple(
(image, model.ind_to_str(truth)))
for g_idx in range(1):
model.train_via_reinforce(sess, batch)
if FLAGS.save and adv_batch % 5 == 0:
saver.save(sess, model_name)
def train_model_lstm(n_neurons, n_steps=28, n_inputs = 28,n_outputs=10, learning_rate=0.0001, n_epochs=100, batch_size=32):
X, y = create_placeholders(n_inputs, n_outputs, n_steps)
# lstm-cell
current_input = tf.unstack(X , n_steps, 1)
lstm_cell_train = LSTMCell(n_neurons, n_inputs)
outputs, states = tf.nn.static_rnn(lstm_cell_train, current_input, dtype=tf.float32)
# fully-connected layer
FC_W = tf.get_variable("FC_W", [n_neurons, n_outputs], initializer = tf.contrib.layers.xavier_initializer(seed=42))
FC_b = tf.get_variable("FC_b", [n_outputs], initializer = tf.zeros_initializer())
Z = tf.add(tf.matmul(outputs[-1], FC_W), FC_b)
#optimization
loss = compute_loss(Z, y)
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
training_op = optimizer.minimize(loss)
correct_prediction = tf.equal(tf.argmax(Z, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
# data-loading
ld=DataLoader()
X_train, Y_train =ld.load_data()
X_val = X_train[55000:]
y_val = Y_train[55000:]
y_val = np.eye(10)[np.asarray(y_val, dtype=np.int32)]
X_val = X_val.reshape((-1, n_steps, n_inputs))
Y_train = np.eye(10)[np.asarray(Y_train, dtype=np.int32)]
X_Batched, Y_Batched = ld.create_batches(X_train[:55000], Y_train[:55000], batch_size)
X_test,y_test=ld.load_data(mode='test')
y_test = np.eye(10)[np.asarray(y_test, dtype=np.int32)]
X_test = X_test.reshape((-1, n_steps, n_inputs))
# for validation
best_validation_accuracy = 0.0
last_improvement = 0
patience = 10
# for saving weights
saver = tf.train.Saver()
weight_filepath = "./weights/lstm/hidden_unit" + str(n_neurons)+ "/model.ckpt"
with tf.Session() as sess:
init.run()
#training
stop = False
for epoch in range(n_epochs):
if stop != True:
for X_batch, y_batch in itertools.izip(X_Batched, Y_Batched):
X_batch = X_batch.reshape((-1, n_steps, n_inputs))
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
print("Train accuracy after %s epochs: %s" %( str(epoch+1), str(acc_train*100) ))
acc_val = accuracy.eval(feed_dict={X: X_val, y: y_val})
print 'Validation Accuracy: ' + str(acc_val*100)
if acc_val > best_validation_accuracy:
last_improvement = epoch
best_validation_accuracy = acc_val
# Save parameters in memory
saver.save(sess, weight_filepath)
if epoch - last_improvement > patience:
print("Early stopping ...")
stop = True
acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
print("Test accuracy: ", acc_test*100)
return acc_test
n_epochs = 200
Hidden_unit_dimension = 300 # Number of units in the hidden layer
output_dimension = 10 # Number of classes of the image
# Load the training and test Data
dl = DataLoader()
X, Y = dl.load_data('train')
X_test, Y_test = dl.load_data('test')
'''Implement mini-batch SGD here'''
# Create an object of the Neural Network Model
nn = NN(X.shape[1], Hidden_unit_dimension, output_dimension)
for current_epoch in range(n_epochs):
for current_batch in range(0, X.shape[0], batch_size):
X_mini, y_mini = dl.create_batches(X, Y, batch_size, shuffle=True)
num_examples = X_mini.shape[0]
hidden_layer, probs = nn.forward(
X_mini) # Compute the hidden layer and softmax layer scores
#print(probs.shape)
loss = nn.compute_loss(probs, X_mini, y_mini, reg) # Compute the loss
if current_batch % (X.shape[0] - 1) == 0:
print("Epoch %d: loss %f" % (current_epoch + 1, loss))
dW1, dW2, db1, db2 = nn.backward(hidden_layer, probs, X_mini, y_mini,
reg)
nn.update_parameters(dW1, dW2, db1, db2, step_size)
'''Test the Training and Tesy accuracy'''
nn.predict(X, Y, mode='train')
def gru_train(num_neurons,
time_step=28,
input_size=28,
target_size=10,
learning_rate=0.0001,
epoch=100,
batch_size=32):
X = tf.placeholder(tf.float32, [None, time_step, input_size])
y = tf.placeholder(tf.int32, [None, target_size])
# lstm-cell
current_input = tf.unstack(X, time_step, 1)
gru_cell_train = GRUCell_train(num_neurons, input_size)
outputs, states = tf.nn.static_rnn(gru_cell_train,
current_input,
dtype=tf.float32)
# fully-connected layer
FC_W = tf.get_variable(
"FC_W", [num_neurons, target_size],
initializer=tf.contrib.layers.xavier_initializer(seed=42))
FC_b = tf.get_variable("FC_b", [target_size],
initializer=tf.zeros_initializer())
Z = tf.add(tf.matmul(outputs[-1], FC_W), FC_b)
#optimizatio
loss = loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=Z, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
correct_prediction = tf.equal(tf.argmax(Z, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
# data-loading
ld = DataLoader()
train_img, train_label = ld.load_data()
train_label = np.eye(10)[np.asarray(train_label, dtype=np.int32)]
minibatch_imged, minibatch_labeled = ld.create_batches(
train_img, train_label, batch_size)
test_img, test_label = ld.load_data(mode='test')
test_label = np.eye(10)[np.asarray(test_label, dtype=np.int32)]
test_img = test_img.reshape((-1, time_step, input_size))
saver = tf.train.Saver()
weight_filepath = "./weights/gru/hidden_unit" + str(
num_neurons) + "/model.ckpt"
with tf.Session() as sess:
init.run()
#training
for epoch in range(epoch):
for minibatch_img, minibatch_label in itertools.izip(
minibatch_imged, minibatch_labeled):
minibatch_img = minibatch_img.reshape(
(-1, time_step, input_size))
sess.run(training_op,
feed_dict={
X: minibatch_img,
y: minibatch_label
})
acc_train = accuracy.eval(feed_dict={
X: minibatch_img,
y: minibatch_label
})
print("Train accuracy after %s epochs: %s" %
(str(epoch + 1), str(acc_train * 100)))
acc_test = accuracy.eval(feed_dict={X: test_img, y: test_label})
#print("Test accuracy: ", acc_test*100)
# Save parameters in memory
saver.save(sess, weight_filepath)
return acc_test
