import math

import numpy as np

import h5py

import matplotlib.pyplot as plt

import tensorflow as tf

from tensorflow.python.framework import ops

from sign_class_lib import *

from PIL import Image

from scipy import ndimage

import time

start = time.time()

#SIGNS Dataset

#

# - **Train set: 1080 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (180 pictures per number).

# - **Test set**: 120 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (20 pictures per number).

print('loading SIGN datasets')

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Example of a picture

#~ index = 0

#~ plt.imshow(X_train_orig[index])

#~ print ("y = " + str(np.squeeze(Y_train_orig[:, index])))

# Flatten the training and test images

X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T

X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T

# Normalize image vectors

X_train = X_train_flatten/255.

X_test = X_test_flatten/255.

# Convert training and test labels to one hot matrices

Y_train = one_hot_encoding(Y_train_orig, 6)

Y_test = one_hot_encoding(Y_test_orig, 6)

print ("train set size" + str(X_train.shape[1]))

print ("test set size" + str(X_test.shape[1]))

print ("X_train shape " + str(X_train.shape))

print ("Y_train shape " + str(Y_train.shape))

print ("X_test shape " + str(X_test.shape))

print ("Y_test shape " + str(Y_test.shape))

print("START TRAINING")

ops.reset_default_graph() # Clears the default graph stack and resets the global default graph. To rerun the model without overwriting tf variables

tf.set_random_seed(1) # 1 to keep consistent results

(n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)

n_y = Y_train.shape[0] # n_y : output size

costs = [] # To keep track of the cost

epoch_n = []

learning_rate = 0.0001

num_epochs = 1500

minibatch_size = 32

seed=3

### CREATE PLACEHOLDERS

X = tf.placeholder(dtype = tf.float32, shape = [n_x, None], name = "X")

Y = tf.placeholder(dtype = tf.float32, shape = [n_y, None], name = "Y")

###INITIALIZE PARAMETERS

W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))

b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())

W2 = tf.get_variable("W2", [12,25], initializer = tf.contrib.layers.xavier_initializer(seed = 1))

b2 = tf.get_variable("b2", [12,1], initializer = tf.zeros_initializer())

W3 = tf.get_variable("W3", [6,12], initializer = tf.contrib.layers.xavier_initializer(seed = 1))

b3 = tf.get_variable("b4", [6,1], initializer = tf.zeros_initializer())

parameters = {"W1": W1, "b1": b1, "W2": W2,"b2": b2, "W3": W3, "b3": b3}

### FORWARD PROPAGATION

#LINEAR/RELU - LINEAR/RELU - LINEAR/SOFTMAX

Z1 = tf.add(tf.matmul(W1,X),b1)

A1 = tf.nn.relu(Z1)

Z2 = tf.add(tf.matmul(W2,A1),b2)

A2 = tf.nn.relu(Z2)

Z3 = tf.add(tf.matmul(W3,A2),b3)

###CALCULATE COST

logits = tf.transpose(Z3)

labels = tf.transpose(Y)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))

### BACKPROPAGATION WITH ADAM

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

### INITIALIZE VARIABLES

init = tf.global_variables_initializer()

### START SESSION

seed=3

with tf.Session() as sess:

# Run the initialization

sess.run(init)

# Do the training loop

for epoch in range(num_epochs):

epoch_cost = 0. # Defines a cost related to an epoch

num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set

seed = seed + 1

minibatches = random_mini_batches(X_train, Y_train, minibatch_size,seed)

for minibatch in minibatches:

# Select a minibatch

(minibatch_X, minibatch_Y) = minibatch

# Run the session: it executes the "optimizer" and the "cost" on a minibatch contained in the feedict (X,Y).

_ , minibatch_cost = sess.run([optimizer,cost], feed_dict={X: minibatch_X, Y: minibatch_Y})

epoch_cost += minibatch_cost / num_minibatches

# Print the cost every epoch

if epoch % 100 == 0:

print ("Cost after epoch %i: %f" % (epoch, epoch_cost))

if epoch % 5 == 0:

costs.append(epoch_cost)

epoch_n.append(epoch)

# plot the cost

plt.plot(epoch_n,costs)

plt.ylabel('cost')

plt.xlabel('epoch')

plt.title("Learning rate =" + str(learning_rate))

plt.savefig('intermediate/parameters_num_epochs_'+str(num_epochs)+'.pdf')

#plt.show()

# lets save the parameters in a variable

parameters = sess.run(parameters)

print ("Parameters have been trained!")

# Calculate the correct predictions

correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))

# Calculate accuracy on the test set

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))

print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

np.save('intermediate/parameters_num_epochs_'+str(num_epochs)+'.npy', parameters)

end = time.time()

print('END TRAINING')

print('time:', end - start, 'sec')

plt.show()