def getInputData(batchsize):
readdata = ReadData()
trainingFiles, testingFiles = readdata.filePathConstructor()
features = readdata.input_pipeline(trainingFiles, batchsize)
example_batch = tf.reshape(features, [-1])
item = tf.string_split(example_batch, delimiter="").values.eval()
return [dict1[alp.decode().lower()] for alp in list(item)]
def main():
readdata = ReadData()
trainingFiles, testingFiles = readdata.filePathConstructor()
features = readdata.input_pipeline(trainingFiles, batch_size)
with tf.Session() as sess:
# Create the graph, etc.
init_op = tf.global_variables_initializer()
sess.run(init_op)
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
dict1 = {
value: (int(key) + 1)
for key, value in enumerate(list(string.ascii_lowercase))
}
dict1[' '] = 0
dict1[';'] = -1
dict1['-'] = -1
vocab_size = len(dict1)
for i in range(1):
example_batch = tf.reshape(features, [-1])
item = tf.string_split(example_batch, delimiter="").values.eval()
chars = [dict1[alp.decode().lower()] for alp in list(item)]
data_size = len(chars)
print('Data has %d characters, %d unique.' %
(data_size, vocab_size))
# # Hyper-parameters
# hidden_size = 100 # hidden layer's size
# seq_length = 25 # number of steps to unroll
# learning_rate = 1e-1
# inputs = tf.placeholder(shape=[None, vocab_size], dtype=tf.float32, name="inputs")
# targets = tf.placeholder(shape=[None, vocab_size], dtype=tf.float32, name="targets")
# init_state = tf.placeholder(shape=[1, hidden_size], dtype=tf.float32, name="state")
# intializer = tf.random_normal_initializer(stddev=1.0)
# with tf.variable_scope("RNN") as scope:
# hs_t = init_state
# ys = []
# for t,xs_t in enumerate(tf.split(inputs,seq_length,axis=0)):
# if t > 0:scope.reuse_variables()
# Wxh = tf.get_variable("Wxh",shape=[vocab_size,hidden_size],dtype=tf.float32,intializer=intializer)
# Whh = tf.get_variable("Whh",shape=[hidden_size,hidden_size],dtype=tf.float32,intializer=intializer)
# Why = tf.get_variable("Why",shape=[hidden_size,vocab_size],dtype=tf.float32,intializer=initializer)
# bh = tf.get_variable("bh",shape=[hidden_size],intializer=intializer)
# by = tf.get_variable("by",shape=[vocab_size],initializer=intializer)
# hs_t = tf.tanh(tf.matmul(xs_t,Wxh) + tf.matmul(hs_t,Whh) + bh)
# ys_t = tf.matmul(hs_t,Why) + by
# ys.append(ys_t)
# h_prev = hs_t
# output_softmax = tf.nn.softmax(ys[-1])
# outputs = tf.concat(ys,axis=0)
# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=targets,logits=outputs))
# #optimizer
# minimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# grad_and_vars = minimizer.compute_gradients(loss)
# pred = RNN(chars,weights,biases)
# # Loss and optimizer
# # cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
# # optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)
# # # Model evaluation
# # correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
# # accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# # print(example_batch)
coord.request_stop()
coord.join(threads)
# Y = predict
# # Cost & Optimizer
# loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y))
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learningrate)
# train_op = optimizer.minimize(loss_op)
# # build accuracy
# correct_pred = tf.equal(tf.argmax(predict,1),tf.argmax(Y,1))
# accuracy = tf.reduce_mean(correct_pred,tf.float32)
# Initiate Global variable
init = tf.global_variables_initializer()
readdata = ReadData()
trainingFiles, testingFiles = readdata.filePathConstructor()
features = readdata.input_pipeline(trainingFiles, batch_size)
# Start training
with tf.Session() as sess:
# init session
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# loop training steps
for step in range(training_steps):
# read input data
example_batch = tf.reshape(features, [-1])
item = tf.string_split(example_batch, delimiter="").values.eval()
batch_x = [dict1[alp.decode().lower()] for alp in list(item)]
