test.py~

import numpy as np
from semval2016_data import *
import tensorflow as tf



(X_train, y_train), (X_test, y_test) = load_a(test_split=0.2,cnn=False,sentiment_embeddings=False,Load_Test=False)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')

print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)



#hyperparameters
lr = 0.1
training_iters = 5
batch_size = 32
display_step = 1

n_inputs = 200
n_steps = 40
n_hidden_units = 100
n_classes = 3


# tf Graph input
x = tf.placeholder(tf.float32,[None,n_steps,n_inputs])
y = tf.placeholder(tf.float32,[None,n_classes])

#Define weights
weights = {
    #(200,100)
    'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),
    #(128,10)
    'out':tf.Variable(tf.random_normal([n_hidden_units,n_classes]))
}
biases = {
    #(128,)
    'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_units,])),
    #(10,)
    'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]))
}

def RNN(X,weights,biases):
    # hidden layer for input to cell
    #############################################
    #X(128 batch,28 steps, 28 inputs)
    # ==> (128*28,28 inputs)
    X = tf.reshape(X,[-1,n_inputs])
    # X_in ==> (128batch*28 steps,128 hidden)
    X_in = tf.matmul(X,weights['in'])+biases['in']
    # X_in ==> (128batch,28 steps,128 hidden)
    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units])

    #cell
    #############################################
    # lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0)
    # lstm cell is devided into two parts(c_state,m_state)
    _init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
    outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)

    # hidden layer for output as the final results
    ###########################################################
    ##results = tf.matmul(states[1],weights['out'])+biases['out']

    # or
    # unpack to list[(batch,outputs)..]*steps
    outputs = tf.unpack(tf.transpose(outputs,[1,0,2])) #state is the last outputs
    results = tf.matmul(outputs[-1],weights['out'])+biases['out']

    return results


pred = RNN(x,weights,biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)


# Compute the number of training steps
step_in_epoch, steps_per_epoch = 0, int(math.floor(len(X_train)/batch_size))
num_steps = steps_per_epoch*config.training_iters
global_step = 0
train_step = 0

# Initialize the TensorFlow session
sess = tf.Session()

# Initialize the session
init = tf.initialize_all_variables()
sess.run(init)

for _ in range(num_steps):

################################################################
########################## TRAINING ############################
################################################################

    index_start = step_in_epoch*batch_size
    index_end   = index_start+batch_size

    # Actual training of the network
    _, train_step, train_loss, learning_rate, train_summary = sess.run(
        [train_op,
         global_step,
         model.loss,
         model.learning_rate,
         model.train_summary_op],
        feed_dict={
            model.inputs:  X_train[index_start:index_end,],
            model.targets: y_train[index_start:index_end,],
        }
    )

print("[%s] Step %05i/%05i, LR = %.2e, Loss = %.5f" %
     (datetime.now().strftime("%Y-%m-%d %H:%M"), train_step, num_steps, learning_rate, train_loss))

# Save summaries to disk
summary_writer.add_summary(train_summary, train_step)

if train_step % 1000 == 0 and train_step > 0:
    path = saver.save(sess, checkpoint_prefix, global_step=train_step)
    print("[%s] Saving TensorFlow model checkpoint to disk." % datetime.now().strftime("%Y-%m-%d %H:%M"))

step_in_epoch += 1

################################################################
############### MODEL TESTING ON EVALUATION DATA ###############
################################################################

if step_in_epoch == steps_per_epoch:

    # End of epoch, check some validation examples
    print("#" * 100)
    print("MODEL TESTING ON VALIDATION DATA (%i examples):" % num_validation)

    for validation_step in range(int(math.floor(num_validation/config.batch_size))):

        index_start = validation_step*config.batch_size
        index_end   = index_start+config.batch_size

        validation_loss, predictions = sess.run([model.loss, model.predictions],
            feed_dict={
                model.inputs:  X_validation[index_start:index_end,],
                model.targets: y_validation[index_start:index_end,],
            }
        )

        # Show a plot of the ground truth and prediction of the singla
        if validation_step == 0:
            plt.clf()
            plt.title("Ground Truth and Predictions")
            plt.plot(y_validation[index_start:index_start+50,0], label="signal 0 (input)")
            plt.plot(predictions[0:50,0], ls='--', label="signal 0 (prediction)")
            plt.plot(y_validation[index_start:index_start+50,1], label="signal 1 (input)")
            plt.plot(predictions[0:50,1], ls='--', label="signal 1 (prediction)")
            legend = plt.legend(frameon=True)
            legend.get_frame().set_facecolor('white')
            plt.draw()
            plt.pause(0.001)

        print("[%s] Validation Step %03i. Loss = %.5f" % (datetime.now().strftime("%Y-%m-%d %H:%M"), validation_step, validation_loss))

    # Reset for next epoch
    step_in_epoch = 0

    # Shuffle training data
    perm = np.arange(num_train)
    np.random.shuffle(perm)
    X_train = X_train[perm]
    y_train = y_train[perm]

    print("#" * 100)

# Destroy the graph and close the session
ops.reset_default_graph()
sess.close()



'''
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    step = 0
    while step*batch_size < training_iters:
        batch_xs,batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size,n_steps,n_inputs])
        sess.run([train_op],feed_dict={
            x:batch_xs,
            y:batch_ys,
        })
        if step % 20 == 0:
            print(sess.run(accuracy,feed_dict={
            x:batch_xs,
            y:batch_ys,
        }))
        step += 1
'''