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import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
tf.reset_default_graph()


    

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# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1)
trainX, trainY = train
testX, testY = test


    

    




---------------------------------------------------------------------------
EOFError                                  Traceback (most recent call last)
<ipython-input-5-b0e5bb757a84> in <module>()
      1 # IMDB Dataset loading
----> 2 train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1)
      3 trainX, trainY = train
      4 testX, testY = test

/Users/ko/anaconda/lib/python3.6/site-packages/tflearn/datasets/imdb.py in load_data(path, n_words, valid_portion, maxlen, sort_by_len)
    102 
    103     train_set = pickle.load(f)
--> 104     test_set = pickle.load(f)
    105     f.close()
    106     if maxlen:

EOFError: Ran out of input

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# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)

# Network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
          batch_size=32)


    

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# Regression data
X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]

# Linear Regression graph
input_ = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
                                metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)

print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
      "*X + " + str(m.get_weights(linear.b)))

print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
# should output (close, not exact) y = [1.5315033197402954,


    

In [3]:

    

import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)


    

    




Extracting mnist/train-images-idx3-ubyte.gz
Extracting mnist/train-labels-idx1-ubyte.gz
Extracting mnist/t10k-images-idx3-ubyte.gz
Extracting mnist/t10k-labels-idx1-ubyte.gz

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