import numpy as np
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc
#train
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc

training_size = 265
img_size = 400*400
training_data = np.empty(shape=(training_size, 400*400))

import glob
i = 0
for filename in glob.glob('D:/TensorPalms/Train/*.jpg'):
    image = misc.imread(filename)
    print(image.shape)
    training_data[i] = image.reshape(-1)
    i+=1


#label


a= [0,0,0,0,0,
    1,1,1,1,1,
    2,2,2,2,2,
    3,3,3,3,3,
    4,4,4,4,4,
    5,5,5,5,5,
    6,6,6,6,6,
    7,7,7,7,7,
    8,8,8,8,8,
    9,9,9,9,9,
    10,10,10,10,10,
    11,11,11,11,11,
    12,12,12,12,12,
    13,13,13,13,13,
    14,14,14,14,14,
    15,15,15,15,15,
    16,16,16,16,16,
    17,17,17,17,17,
    18,18,18,18,18,
    19,19,19,19,19,
    20,20,20,20,20,
    21,21,21,21,21,
    22,22,22,22,22,
    23,23,23,23,23,
    24,24,24,24,24,
    25,25,25,25,25,
    26,26,26,26,26,
    27,27,27,27,27,
    28,28,28,28,28,
    29,29,29,29,29,
    30,30,30,30,30,
    31,31,31,31,31,
    32,32,32,32,32,
    33,33,33,33,33,
    34,34,34,34,34,
    35,35,35,35,35,
    36,36,36,36,36,
    37,37,37,37,37,
    38,38,38,38,38,
    39,39,39,39,39,
    40,40,40,40,40,
    41,41,41,41,41,
    42,42,42,42,42,
    43,43,43,43,43,
    44,44,44,44,44,
    45,45,45,45,45,
    46,46,46,46,46,
    47,47,47,47,47,
    48,48,48,48,48,
    49,49,49,49,49,
    50,50,50,50,50,
    51,51,51,51,51,
    52,52,52,52,52,]



b = tf.one_hot(a,53)
sess = tf.Session()
sess.run(b)

print(b.shape)


#test
import tensorflow as tf
test_size = 159
img_size = 400*400

test_images = np.empty(shape=(test_size,400*400))
import glob
i = 0
for filename in glob.glob('D:/TensorPalms/Test*.jpg'):
    image = misc.imread(filename)
    test_images[i] = image
    i+=1
print(test_images[0].shape)

c= [0,0,0,
    1,1,1,
    2,2,2,
    3,3,3,
    4,4,4,
    5,5,5,
    6,6,6,
    7,7,7,
    8,8,8,
    9,9,9,
    10,10,10,
    11,11,11,
    12,12,12,
    13,13,13,
    14,14,14,
    15,15,15,
    16,16,16,
    17,17,17,
    18,18,18,
    19,19,19,
    20,20,20,
    21,21,21,
    22,22,22,
    23,23,23,
    24,24,24,
    25,25,25,
    26,26,26,
    27,27,27,
    28,28,28,
    29,29,29,
    30,30,30,
    31,31,31,
    32,32,32,
    33,33,33,
    34,34,34,
    35,35,35,
    36,36,36,
    37,37,37,
    38,38,38,
    39,39,39,
    40,40,40,
    41,41,41,
    42,42,42,
    43,43,43,
    44,44,44,
    45,45,45,
    46,46,46,
    47,47,47,
    48,48,48,
    49,49,49,
    50,50,50,
    51,51,51,
    52,52,52]


test_labels = tf.one_hot(c,53)
sess = tf.Session()
sess.run(test_labels)


import tensorflow as tf
tf.reset_default_graph()
from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression


network = input_data(shape=[None, 400, 400,3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 53, activation='softmax')
network = regression(network, optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                    max_checkpoints=1, tensorboard_verbose=2)

model.fit(training_data, b, n_epoch=1000, validation_set=(test_images, test_labels),
          shuffle=True, show_metric=True, batch_size=64, snapshot_step=200,
          snapshot_epoch=False, run_id='alexnet_test')