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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 GenSyntheticMNSITFixedWidthModule as GenDataset
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
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# Consts
DATSET_SIZE = 10000
WIDTH_NUMS = 2
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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))
print (index_offset + labels_dense.ravel())
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
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# Get the dataset
X, Y = GenDataset.getDataSet(WIDTH_NUMS, DATSET_SIZE)
X = X.reshape([-1, 28, 28 * WIDTH_NUMS, 1])
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print (X.shape)
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# Generate validation set
ratio = 0.8 # Train/Test set
randIdx = np.random.random(DATSET_SIZE) <= 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]) for idx in range(Y_train.shape[1])]
Y_test = [dense_to_one_hot(Y_test[:,idx]) for idx in range(Y_test.shape[1])]
del X, Y # release some space
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# Test a sample data
%matplotlib inline
idx = np.random.randint(0,X_train.shape[0])
print ([Y_train[i][idx] for i in range(len(Y_train))])
print (X_train[idx].shape)
plt.imshow(np.squeeze(X_train[idx]), cmap = 'gray')
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# Building convolutional network
network = input_data(shape=[None, 28, 28 * WIDTH_NUMS, 1], name='input')
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)
fc_1 = fully_connected(network, 128, activation='tanh')
fc_1 = dropout(fc_1, 0.8)
fc_2 = fully_connected(network, 128, activation='tanh')
fc_2 = dropout(fc_2, 0.8)
softmax1 = fully_connected(fc_1, 10, activation='softmax')
softmax2 = fully_connected(fc_2, 10, activation='softmax')
network1 = regression(softmax1, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target1')
network2 = regression(softmax2, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target2')
network = tflearn.merge([network1, network2], mode='elemwise_sum')
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model = tflearn.DNN(network, tensorboard_verbose=1)
model.fit({'input': X_train}, {'target1': Y_train[0], 'target2': Y_train[1]},
validation_set= (X_test, [Y_test[0], Y_test[1]]), n_epoch=5, snapshot_step=100, show_metric=True, run_id='convnet_mnist_')
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