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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
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import tensorflow as tf
import h5py
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
%matplotlib inline
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IMG_WIDTH = 32 # Side for each transformed Image
IMG_HEIGHT = 32
IMG_DEPTH = 1 # RGB files
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NUM_LABELS = 3
h5FileName = 'svhn_' + str(NUM_LABELS) + '.h5'
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data = h5py.File(h5FileName)
imgs = np.array(data['images']).astype(float)
labels = np.array(data['digits'])
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print (type(imgs))
print (labels.shape)
print (imgs.shape)
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print (labels[0])
plt.imshow(imgs[0], cmap='gray')
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def dense_to_one_hot(labels_dense, num_classes=10):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = np.arange(num_labels) * num_classes
labels_one_hot = np.zeros((num_labels, num_classes))
index_update = [int(x) for x in index_offset + labels_dense.ravel()]
labels_one_hot.flat[index_update] = 1
return labels_one_hot
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# Get the dataset
X = imgs.reshape([-1, IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH])
Y = labels
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# Generate validation set
ratio = 0.9 # Train/Test set
randIdx = np.random.random(imgs.shape[0]) <= ratio
#print (sum(map(lambda x: int(x), randIdx)))
X_train = X[randIdx]
Y_train = Y[randIdx]
X_test = X[randIdx == False]
Y_test = Y[randIdx == False]
Y_train = [dense_to_one_hot(Y_train[:,idx], num_classes= 10) for idx in range(Y_train.shape[1])]
Y_test = [dense_to_one_hot(Y_test[:,idx], num_classes= 10) for idx in range(Y_test.shape[1])]
#del X, Y # release some space
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print (X_train.shape)
print (Y_train[0].shape)
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print (np.mean(Y_train[1], axis = 0))
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# Building convolutional network
with tf.Graph().as_default():
# Building convolutional network
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = ImageAugmentation()
#img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
network = input_data(shape=[None, IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH], name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
#network = fully_connected(network, 1024, activation='tanh')
#network = dropout(network, 0.8)
#network = fully_connected(network, 1024, activation='tanh')
#network = dropout(network, 0.8)
fc_1 = fully_connected(network, 1024, activation='tanh')
#fc_1 = dropout(fc_1, 0.8)
fc_2 = fully_connected(network, 1024, activation='tanh')
#fc_2 = dropout(fc_2, 0.8)
fc_3 = fully_connected(network, 1024, activation='tanh')
#fc_3 = dropout(fc_3, 0.8)
softmax1 = fully_connected(fc_1, 10, activation='softmax')
softmax2 = fully_connected(fc_2, 10, activation='softmax')
softmax3 = fully_connected(fc_3, 10, activation='softmax')
#softmax1 = fully_connected(network, 10, activation='softmax')
#softmax2 = fully_connected(network, 10, activation='softmax')
#softmax3 = fully_connected(network, 10, activation='softmax')
network1 = regression(softmax1, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target0')
network2 = regression(softmax2, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target1')
network3 = regression(softmax3, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target2')
network = tflearn.merge([network1, network2, network3], mode='elemwise_sum')
model = tflearn.DNN(network, tensorboard_verbose=3)
feedTrainDict = {'target'+ str(i): Y_train[i] for i in range(NUM_LABELS)}
#feedTrainDict = {'target0': Y_train[0]}
feedTestList = [Y_test[i] for i in range(NUM_LABELS)]
#feedTestList = Y_test[0]
model.fit({'input': X_train}, feedTrainDict,
validation_set= (X_test, feedTestList), n_epoch=10, snapshot_step=100, show_metric=True, run_id='convnet_svhn')
#model.fit({'input': X_train}, feedTrainDict, n_epoch=1, show_metric=True, run_id='convnet_mnist')
#model.fit({'input': X_train}, {'target0': Y_train[1]}, n_epoch=1, show_metric=True, run_id='convnet_mnist')
numImgEachAxis = 8
f,ax = plt.subplots(numImgEachAxis, numImgEachAxis, figsize=(10,10))
for i in range(numImgEachAxis):
for j in range(numImgEachAxis):
res = np.array([np.argmax(x) for x in model.predict([X_train[i*numImgEachAxis + j]])])
print (str(i) + ',' + str(j) + ' -> ' +str(res))
#ax[i][j].set_title(str([np.round(x,2) for x in res]))
ax[i][j].imshow(X_train[i*numImgEachAxis + j].reshape((IMG_HEIGHT,IMG_WIDTH)) ,cmap = 'gray')
plt.show() # or display.display(plt.gcf()) if you prefer
# print (model.evaluate(X_test,feedTestList))
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