<filename>analyseNerves.py<gh_stars>1-10
import NervesFile
from NervesFile import Nerves
import helpers
import tensorflow as tf
from tensorflow.models.rnn import rnn, rnn_cell
import numpy as np

class inference_hidden0(Nerves):
	def __init__(self):
		Nerves.__init__(self)


############main method#########################
model0 = inference_hidden0()
model0.trainNervesPickle = 'fullTrainImages.pickle'
model0.prepareDataTolearn()
#model0.testWeightsOnRealImages()

'''
To classify images using a reccurent neural network, we consider every image row as a sequence of pixels.
Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.
'''

# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

# Network Parameters
n_steps = model0.fullImageHeight # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 2 
# tf Graph input
x = tf.placeholder("float", [None, n_steps, model0.fullImageWidth])
istate = tf.placeholder("float", [None, 2*n_hidden]) #state & cell => 2x n_hidden
y = tf.placeholder("float", [None, n_classes])

# Define weights
weights = {
    'hidden': tf.Variable(tf.random_normal([model0.fullImageWidh, n_hidden])), # Hidden layer weights
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([n_hidden])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}
def RNN(_X, _istate, _weights, _biases):

    # input shape: (batch_size, n_steps, n_input)
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    # Reshape to prepare input to hidden activation
    _X = tf.reshape(_X, [-1, model0.fullImageWidth]) # (n_steps*batch_size, n_input)
    # Linear activation
    _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']

    # Define a lstm cell with tensorflow
    lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
    # Split data because rnn cell needs a list of inputs for the RNN inner loop
    _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)

    # Get lstm cell output
    outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)

    # Linear activation
    # Get inner loop last output
    return tf.matmul(outputs[-1], _weights['out']) + _biases['out']
pred = RNN(x, istate, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_xs, batch_ys = model0.fillFeedDict(model0.train_dataset, model0.train_labels, step, model0.batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_xs = batch_xs.reshape((batch_size, n_steps, model0.fullImageWidth))
        # Fit training using batch data
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,
                                       istate: np.zeros((batch_size, 2*n_hidden))})
        if step % display_step == 0:
            # Calculate batch accuracy
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,
                                                istate: np.zeros((batch_size, 2*n_hidden))})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,
                                             istate: np.zeros((batch_size, 2*n_hidden))})
            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \
                  ", Training Accuracy= " + "{:.5f}".format(acc)
        step += 1
    print "Optimization Finished!"
    # Calculate accuracy for 256 mnist test images
    test_len = 256
    test_data = model0.test_dataset.reshape((-1, n_steps, model0.fullImageWidth))
    test_label = model0.test_labels[:test_len]
    print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label,
                                                             istate: np.zeros((test_len, 2*n_hidden))})
