1. Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/re
uters.npz
2113536/2110848 [==============================] - 3s 2us/step
2121728/2110848 [==============================] - 3s 2us/step
C:Userswaleeanaconda3libsite-packageskerasdatasetsreuters.py:143: VisibleDep
recationWarning: Creating an ndarray from ragged nested sequences (which is a list-o
r-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecat
ed. If you meant to do this, you must specify 'dtype=object' when creating the ndarr
ay
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
C:Userswaleeanaconda3libsite-packageskerasdatasetsreuters.py:144: VisibleDep
recationWarning: Creating an ndarray from ragged nested sequences (which is a list-o
r-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecat
ed. If you meant to do this, you must specify 'dtype=object' when creating the ndarr
ay
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
8982
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In [1]: from keras.datasets import reuters
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=1
In [2]: len(train_data)
Out[2]:
In [3]: len(test_data)
Out[3]:
In [4]: train_data[10]
Out[4]:
In [5]: word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# Offset indices by 3

2. Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/re
uters_word_index.json
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565248/550378 [==============================] - 1s 2us/step
'? ? ? said as a result of its december acquisition of space co it expects earnings
per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company
said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and ren
tal operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per
share this year should be 2 50 to three dlrs reuter 3'
3
# because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "u
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[
In [6]: decoded_newswire
Out[6]:
In [7]: train_labels[10]
Out[7]:
In [8]: import numpy as np
def vectorize_sequences(sequences, dimension=10000):
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
return results
# Our vectorized training data
x_train = vectorize_sequences(train_data)
# Our vectorized test data
x_test = vectorize_sequences(test_data)
In [9]: def to_one_hot(labels, dimension=46):
results = np.zeros((len(labels), dimension))
for i, label in enumerate(labels):
results[i, label] = 1.
return results
# Our vectorized training labels
one_hot_train_labels = to_one_hot(train_labels)
# Our vectorized test labels
one_hot_test_labels = to_one_hot(test_labels)
In [10]: from keras.utils.np_utils import to_categorical
one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)
In [11]: from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))
In [12]: model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
In [13]: x_val = x_train[:1000]
partial_x_train = x_train[1000:]
y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]
In [14]: history = model.fit(partial_x_train,
partial_y_train,

3. Epoch 1/20
16/16 [==============================] - 39s 125ms/step - loss: 3.2245 - accuracy:
0.3478 - val_loss: 1.7983 - val_accuracy: 0.6600
Epoch 2/20
16/16 [==============================] - 1s 41ms/step - loss: 1.5213 - accuracy: 0.7
100 - val_loss: 1.3048 - val_accuracy: 0.7230
Epoch 3/20
16/16 [==============================] - 1s 34ms/step - loss: 1.0804 - accuracy: 0.7
787 - val_loss: 1.1374 - val_accuracy: 0.7580
Epoch 4/20
16/16 [==============================] - 1s 35ms/step - loss: 0.8291 - accuracy: 0.8
275 - val_loss: 1.0291 - val_accuracy: 0.7910
Epoch 5/20
16/16 [==============================] - 1s 35ms/step - loss: 0.6616 - accuracy: 0.8
663 - val_loss: 0.9549 - val_accuracy: 0.7970
Epoch 6/20
16/16 [==============================] - 1s 38ms/step - loss: 0.5123 - accuracy: 0.8
973 - val_loss: 0.9079 - val_accuracy: 0.8160
Epoch 7/20
16/16 [==============================] - 1s 38ms/step - loss: 0.4107 - accuracy: 0.9
206 - val_loss: 0.9038 - val_accuracy: 0.8100
Epoch 8/20
16/16 [==============================] - 1s 37ms/step - loss: 0.3268 - accuracy: 0.9
343 - val_loss: 0.8954 - val_accuracy: 0.8040
Epoch 9/20
16/16 [==============================] - 1s 35ms/step - loss: 0.2674 - accuracy: 0.9
441 - val_loss: 0.9077 - val_accuracy: 0.8080
Epoch 10/20
16/16 [==============================] - 1s 39ms/step - loss: 0.2275 - accuracy: 0.9
504 - val_loss: 0.8904 - val_accuracy: 0.8220
Epoch 11/20
16/16 [==============================] - 1s 37ms/step - loss: 0.1932 - accuracy: 0.9
551 - val_loss: 0.8983 - val_accuracy: 0.8190
Epoch 12/20
16/16 [==============================] - 1s 37ms/step - loss: 0.1673 - accuracy: 0.9
572 - val_loss: 0.9229 - val_accuracy: 0.8190
Epoch 13/20
16/16 [==============================] - 1s 35ms/step - loss: 0.1513 - accuracy: 0.9
583 - val_loss: 0.9595 - val_accuracy: 0.8070
Epoch 14/20
16/16 [==============================] - 1s 35ms/step - loss: 0.1424 - accuracy: 0.9
579 - val_loss: 0.9638 - val_accuracy: 0.8150
Epoch 15/20
16/16 [==============================] - 1s 36ms/step - loss: 0.1248 - accuracy: 0.9
623 - val_loss: 0.9832 - val_accuracy: 0.8070
Epoch 16/20
16/16 [==============================] - 1s 36ms/step - loss: 0.1246 - accuracy: 0.9
571 - val_loss: 1.0030 - val_accuracy: 0.8120
Epoch 17/20
16/16 [==============================] - 1s 35ms/step - loss: 0.1177 - accuracy: 0.9
608 - val_loss: 1.0174 - val_accuracy: 0.8040
Epoch 18/20
16/16 [==============================] - 1s 36ms/step - loss: 0.1055 - accuracy: 0.9
625 - val_loss: 1.0484 - val_accuracy: 0.8100
Epoch 19/20
16/16 [==============================] - 1s 34ms/step - loss: 0.1068 - accuracy: 0.9
614 - val_loss: 1.0720 - val_accuracy: 0.8040
Epoch 20/20
16/16 [==============================] - 1s 37ms/step - loss: 0.1055 - accuracy: 0.9
583 - val_loss: 1.0762 - val_accuracy: 0.8000
epochs=20,
batch_size=512,
validation_data=(x_val, y_val))
In [15]: import matplotlib.pyplot as plt
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)

4. plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
In [16]: plt.clf() # clears the figure
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
In [17]: model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(partial_x_train,
partial_y_train,

5. Epoch 1/8
16/16 [==============================] - 3s 67ms/step - loss: 3.0656 - accuracy: 0.4
297 - val_loss: 1.6959 - val_accuracy: 0.6300
Epoch 2/8
16/16 [==============================] - 1s 32ms/step - loss: 1.5030 - accuracy: 0.6
813 - val_loss: 1.2944 - val_accuracy: 0.7150
Epoch 3/8
16/16 [==============================] - 1s 36ms/step - loss: 1.0861 - accuracy: 0.7
640 - val_loss: 1.1250 - val_accuracy: 0.7540
Epoch 4/8
16/16 [==============================] - 1s 39ms/step - loss: 0.8546 - accuracy: 0.8
190 - val_loss: 1.0358 - val_accuracy: 0.7760
Epoch 5/8
16/16 [==============================] - 1s 39ms/step - loss: 0.6699 - accuracy: 0.8
599 - val_loss: 0.9529 - val_accuracy: 0.7990
Epoch 6/8
16/16 [==============================] - 1s 37ms/step - loss: 0.5317 - accuracy: 0.8
909 - val_loss: 0.9138 - val_accuracy: 0.8120
Epoch 7/8
16/16 [==============================] - 1s 38ms/step - loss: 0.4173 - accuracy: 0.9
173 - val_loss: 0.9121 - val_accuracy: 0.8040
Epoch 8/8
16/16 [==============================] - 1s 38ms/step - loss: 0.3313 - accuracy: 0.9
337 - val_loss: 0.8866 - val_accuracy: 0.8190
71/71 [==============================] - 0s 3ms/step - loss: 0.9642 - accuracy: 0.78
63
[0.964229941368103, 0.7862867116928101]
0.18699910952804988
(46,)
1.0000001
3
epochs=8,
batch_size=512,
validation_data=(x_val, y_val))
results = model.evaluate(x_test, one_hot_test_labels)
In [18]: results
Out[18]:
In [19]: import copy
test_labels_copy = copy.copy(test_labels)
np.random.shuffle(test_labels_copy)
float(np.sum(np.array(test_labels) == np.array(test_labels_copy))) / len(test_labels
Out[19]:
In [20]: predictions = model.predict(x_test)
In [21]: predictions[0].shape
Out[21]:
In [22]: np.sum(predictions[0])
Out[22]:
In [23]: np.argmax(predictions[0])
Out[23]:
In [24]: y_train = np.array(train_labels)
y_test = np.array(test_labels)
In [25]: model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=[
In [26]: model = models.Sequential()

6. Epoch 1/20
63/63 [==============================] - 4s 32ms/step - loss: 3.4182 - accuracy: 0.1
167 - val_loss: 2.5418 - val_accuracy: 0.4070
Epoch 2/20
63/63 [==============================] - 1s 16ms/step - loss: 2.3245 - accuracy: 0.4
161 - val_loss: 2.0374 - val_accuracy: 0.4260
Epoch 3/20
63/63 [==============================] - 1s 16ms/step - loss: 1.8282 - accuracy: 0.4
539 - val_loss: 1.7261 - val_accuracy: 0.4410
Epoch 4/20
63/63 [==============================] - 1s 16ms/step - loss: 1.4547 - accuracy: 0.5
176 - val_loss: 1.4957 - val_accuracy: 0.6370
Epoch 5/20
63/63 [==============================] - 1s 17ms/step - loss: 1.2671 - accuracy: 0.6
758 - val_loss: 1.4441 - val_accuracy: 0.6600
Epoch 6/20
63/63 [==============================] - 1s 15ms/step - loss: 1.1477 - accuracy: 0.7
117 - val_loss: 1.4194 - val_accuracy: 0.6750
Epoch 7/20
63/63 [==============================] - 1s 21ms/step - loss: 1.0301 - accuracy: 0.7
430 - val_loss: 1.4107 - val_accuracy: 0.6880
Epoch 8/20
63/63 [==============================] - 1s 18ms/step - loss: 0.9645 - accuracy: 0.7
553 - val_loss: 1.4553 - val_accuracy: 0.6880
Epoch 9/20
63/63 [==============================] - 1s 17ms/step - loss: 0.9077 - accuracy: 0.7
690 - val_loss: 1.4827 - val_accuracy: 0.6780
Epoch 10/20
63/63 [==============================] - 1s 19ms/step - loss: 0.8553 - accuracy: 0.7
733 - val_loss: 1.4980 - val_accuracy: 0.6870
Epoch 11/20
63/63 [==============================] - 1s 19ms/step - loss: 0.8322 - accuracy: 0.7
800 - val_loss: 1.5350 - val_accuracy: 0.6820
Epoch 12/20
63/63 [==============================] - 1s 21ms/step - loss: 0.7728 - accuracy: 0.7
883 - val_loss: 1.5694 - val_accuracy: 0.6790
Epoch 13/20
63/63 [==============================] - 1s 17ms/step - loss: 0.7678 - accuracy: 0.7
847 - val_loss: 1.6703 - val_accuracy: 0.6820
Epoch 14/20
63/63 [==============================] - 1s 20ms/step - loss: 0.7472 - accuracy: 0.7
846 - val_loss: 1.7068 - val_accuracy: 0.6820
Epoch 15/20
63/63 [==============================] - 1s 22ms/step - loss: 0.7523 - accuracy: 0.7
822 - val_loss: 1.7743 - val_accuracy: 0.6810
Epoch 16/20
63/63 [==============================] - 1s 19ms/step - loss: 0.7087 - accuracy: 0.7
904 - val_loss: 1.8168 - val_accuracy: 0.6750
Epoch 17/20
63/63 [==============================] - 1s 14ms/step - loss: 0.7009 - accuracy: 0.7
919 - val_loss: 1.8658 - val_accuracy: 0.6870
Epoch 18/20
63/63 [==============================] - 1s 19ms/step - loss: 0.6559 - accuracy: 0.8
070 - val_loss: 1.9383 - val_accuracy: 0.6790
Epoch 19/20
63/63 [==============================] - 1s 15ms/step - loss: 0.6627 - accuracy: 0.8
041 - val_loss: 2.0031 - val_accuracy: 0.6810
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(4, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(partial_x_train,
partial_y_train,
epochs=20,
batch_size=128,
validation_data=(x_val, y_val))

7. Epoch 20/20
63/63 [==============================] - 1s 14ms/step - loss: 0.6398 - accuracy: 0.8
039 - val_loss: 1.9840 - val_accuracy: 0.6790
<keras.callbacks.History at 0x1328abf6910>
Out[26]:
In [ ]: