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consistency updates
1 parent 48c9f8f commit 13f8689

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+441
-446
lines changed

‎ch13/ch13_part1.ipynb

+114-78
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‎ch13/ch13_part1.py

+23-2
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@@ -212,7 +212,7 @@
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print(t.numpy())
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215-
t_splits = tf.split(t, [3, 2])
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t_splits = tf.split(t, num_or_size_splits=[3, 2])
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[item.numpy() for item in t_splits]
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@@ -345,6 +345,17 @@
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tf.random.set_seed(1)
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## Order 1: shuffle -> batch -> repeat
351+
ds = ds_joint.shuffle(4).batch(2).repeat(3)
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353+
for i,(batch_x, batch_y) in enumerate(ds):
354+
print(i, batch_x.shape, batch_y.numpy())
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tf.random.set_seed(1)
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## Order 1: shuffle -> batch -> repeat
@@ -356,6 +367,17 @@
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tf.random.set_seed(1)
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## Order 2: batch -> shuffle -> repeat
373+
ds = ds_joint.batch(2).shuffle(4).repeat(3)
374+
375+
for i,(batch_x, batch_y) in enumerate(ds):
376+
print(i, batch_x.shape, batch_y.numpy())
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tf.random.set_seed(1)
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## Order 2: batch -> shuffle -> repeat
@@ -560,7 +582,6 @@ def load_and_preprocess(path, label):
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ax.imshow(image[:, :, 0], cmap='gray_r')
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ax.set_title('{}'.format(label), size=15)
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#plt.savefig('ch13-mnist-new.png')
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plt.show()
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‎ch13/ch13_part2.ipynb

+220-225
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‎ch13/ch13_part2.py

+17-17
Original file line numberDiff line numberDiff line change
@@ -263,19 +263,19 @@ def train(model, inputs, outputs, learning_rate):
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model = tf.keras.Sequential([
266+
iris_model = tf.keras.Sequential([
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tf.keras.layers.Dense(16, activation='sigmoid',
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name='fc1', input_shape=(4,)),
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tf.keras.layers.Dense(3, name='fc2', activation='softmax')])
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model.summary()
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iris_model.summary()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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iris_model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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@@ -291,9 +291,9 @@ def train(model, inputs, outputs, learning_rate):
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ds_train = ds_train.prefetch(buffer_size=1000)
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history = model.fit(ds_train, epochs=num_epochs,
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steps_per_epoch=steps_per_epoch,
296-
verbose=0)
294+
history = iris_model.fit(ds_train, epochs=num_epochs,
295+
steps_per_epoch=steps_per_epoch,
296+
verbose=0)
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@@ -322,30 +322,30 @@ def train(model, inputs, outputs, learning_rate):
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results = model.evaluate(ds_test.batch(50), verbose=0)
325+
results = iris_model.evaluate(ds_test.batch(50), verbose=0)
326326
print('Test loss: {:.4f} Test Acc.: {:.4f}'.format(*results))
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# ### Saving and reloading the trained model
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model.save('iris-classifier.h5',
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overwrite=True,
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include_optimizer=True,
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save_format='h5')
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iris_model.save('iris-classifier.h5',
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overwrite=True,
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include_optimizer=True,
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save_format='h5')
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model_new = tf.keras.models.load_model('iris-classifier.h5')
341+
iris_model_new = tf.keras.models.load_model('iris-classifier.h5')
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model_new.summary()
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iris_model_new.summary()
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results = model_new.evaluate(ds_test.batch(50), verbose=0)
348+
results = iris_model_new.evaluate(ds_test.batch(50), verbose=0)
349349
print('Test loss: {:.4f} Test Acc.: {:.4f}'.format(*results))
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351351

@@ -363,7 +363,7 @@ def train(model, inputs, outputs, learning_rate):
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366-
model.to_json()
366+
iris_model_new.to_json()
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# ## Choosing activation functions for multilayer neural networks

‎ch14/ch14_part1.ipynb

+8-8
Original file line numberDiff line numberDiff line change
@@ -35,9 +35,9 @@
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"output_type": "stream",
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"text": [
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"Sebastian Raschka & Vahid Mirjalili \n",
38-
"last updated: 2019-12-04 \n",
38+
"last updated: 2019-12-06 \n",
3939
"\n",
40-
"numpy 1.17.3\n",
40+
"numpy 1.17.4\n",
4141
"scipy 1.3.1\n",
4242
"matplotlib 3.1.0\n",
4343
"tensorflow 2.0.0\n"
@@ -783,8 +783,8 @@
783783
],
784784
"source": [
785785
"model = tf.keras.Sequential()\n",
786-
"model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
787-
"model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
786+
"model.add(tf.keras.layers.Dense(units=16, activation='relu'))\n",
787+
"model.add(tf.keras.layers.Dense(units=32, activation='relu'))\n",
788788
"\n",
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"## late variable creation\n",
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"model.build(input_shape=(None, 4))\n",
@@ -1487,9 +1487,9 @@
14871487
"\n",
14881488
"model = tf.keras.Sequential([\n",
14891489
" NoisyLinear(4, noise_stddev=0.1),\n",
1490-
" tf.keras.layers.Dense(4, activation='relu'),\n",
1491-
" tf.keras.layers.Dense(4, activation='relu'),\n",
1492-
" tf.keras.layers.Dense(1, activation='sigmoid')])\n",
1490+
" tf.keras.layers.Dense(units=4, activation='relu'),\n",
1491+
" tf.keras.layers.Dense(units=4, activation='relu'),\n",
1492+
" tf.keras.layers.Dense(units=1, activation='sigmoid')])\n",
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"\n",
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"model.build(input_shape=(None, 2))\n",
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"model.summary()\n",
@@ -1557,7 +1557,7 @@
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"output_type": "stream",
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"text": [
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"[NbConvertApp] Converting notebook ch14_part1.ipynb to script\n",
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"[NbConvertApp] Writing 19958 bytes to ch14_part1.py\n"
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"[NbConvertApp] Writing 19996 bytes to ch14_part1.py\n"
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]
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}
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],

‎ch14/ch14_part1.py

+5-5
Original file line numberDiff line numberDiff line change
@@ -341,8 +341,8 @@ def compute_z(x):
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343343
model = tf.keras.Sequential()
344-
model.add(tf.keras.layers.Dense(16, activation='relu'))
345-
model.add(tf.keras.layers.Dense(32, activation='relu'))
344+
model.add(tf.keras.layers.Dense(units=16, activation='relu'))
345+
model.add(tf.keras.layers.Dense(units=32, activation='relu'))
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## late variable creation
348348
model.build(input_shape=(None, 4))
@@ -729,9 +729,9 @@ def get_config(self):
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730730
model = tf.keras.Sequential([
731731
NoisyLinear(4, noise_stddev=0.1),
732-
tf.keras.layers.Dense(4, activation='relu'),
733-
tf.keras.layers.Dense(4, activation='relu'),
734-
tf.keras.layers.Dense(1, activation='sigmoid')])
732+
tf.keras.layers.Dense(units=4, activation='relu'),
733+
tf.keras.layers.Dense(units=4, activation='relu'),
734+
tf.keras.layers.Dense(units=1, activation='sigmoid')])
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model.build(input_shape=(None, 2))
737737
model.summary()

‎ch14/ch14_part2.ipynb

+9-57
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@@ -1002,13 +1002,7 @@
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"INFO:tensorflow:global_step/sec: 567.788\n",
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"INFO:tensorflow:loss = 240.40808, step = 4800 (0.176 sec)\n",
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"INFO:tensorflow:global_step/sec: 587.168\n",
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"INFO:tensorflow:loss = 319.34958, step = 4900 (0.170 sec)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 319.34958, step = 4900 (0.170 sec)\n",
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"INFO:tensorflow:global_step/sec: 613.08\n",
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"INFO:tensorflow:loss = 270.3896, step = 5000 (0.163 sec)\n",
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"INFO:tensorflow:global_step/sec: 615.585\n",
@@ -1174,13 +1168,7 @@
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"INFO:tensorflow:global_step/sec: 653.213\n",
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"INFO:tensorflow:loss = 69.10744, step = 13100 (0.153 sec)\n",
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"INFO:tensorflow:global_step/sec: 661.769\n",
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"INFO:tensorflow:loss = 66.7493, step = 13200 (0.151 sec)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 66.7493, step = 13200 (0.151 sec)\n",
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"INFO:tensorflow:global_step/sec: 653.19\n",
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"INFO:tensorflow:loss = 50.434845, step = 13300 (0.153 sec)\n",
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"INFO:tensorflow:global_step/sec: 662.245\n",
@@ -1345,13 +1333,7 @@
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"INFO:tensorflow:loss = 26.513557, step = 21300 (0.178 sec)\n",
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"INFO:tensorflow:global_step/sec: 640.564\n",
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"INFO:tensorflow:loss = 21.903809, step = 21400 (0.156 sec)\n",
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"INFO:tensorflow:global_step/sec: 647.2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:global_step/sec: 647.2\n",
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"INFO:tensorflow:loss = 30.5953, step = 21500 (0.154 sec)\n",
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"INFO:tensorflow:global_step/sec: 638.928\n",
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"INFO:tensorflow:loss = 27.62001, step = 21600 (0.157 sec)\n",
@@ -1516,13 +1498,7 @@
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"INFO:tensorflow:global_step/sec: 638.647\n",
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"INFO:tensorflow:loss = 53.59935, step = 29600 (0.157 sec)\n",
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"INFO:tensorflow:global_step/sec: 641.218\n",
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"INFO:tensorflow:loss = 1.7887146, step = 29700 (0.156 sec)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 1.7887146, step = 29700 (0.156 sec)\n",
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"INFO:tensorflow:global_step/sec: 632.812\n",
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"INFO:tensorflow:loss = 9.185707, step = 29800 (0.158 sec)\n",
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"INFO:tensorflow:global_step/sec: 636.626\n",
@@ -1687,13 +1663,7 @@
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"INFO:tensorflow:loss = 6.9836364, step = 37800 (0.157 sec)\n",
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"INFO:tensorflow:global_step/sec: 640.438\n",
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"INFO:tensorflow:loss = 3.7009566, step = 37900 (0.156 sec)\n",
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"INFO:tensorflow:global_step/sec: 652.57\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:global_step/sec: 652.57\n",
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"INFO:tensorflow:loss = 15.457409, step = 38000 (0.153 sec)\n",
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"INFO:tensorflow:global_step/sec: 641.224\n",
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"INFO:tensorflow:loss = 15.640904, step = 38100 (0.156 sec)\n",
@@ -2007,13 +1977,7 @@
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"INFO:tensorflow:global_step/sec: 388.812\n",
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"INFO:tensorflow:loss = 0.2290253, step = 4380 (0.273 sec)\n",
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"INFO:tensorflow:global_step/sec: 373.834\n",
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"INFO:tensorflow:loss = 0.24748756, step = 4480 (0.270 sec)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 0.24748756, step = 4480 (0.270 sec)\n",
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"INFO:tensorflow:global_step/sec: 373.023\n",
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"INFO:tensorflow:loss = 0.2879139, step = 4580 (0.275 sec)\n",
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"INFO:tensorflow:global_step/sec: 364.77\n",
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"INFO:tensorflow:global_step/sec: 303.166\n",
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"INFO:tensorflow:loss = 0.018593434, step = 12580 (0.310 sec)\n",
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"INFO:tensorflow:global_step/sec: 307.677\n",
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"INFO:tensorflow:loss = 0.009453268, step = 12680 (0.318 sec)\n"
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]
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{
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 0.009453268, step = 12680 (0.318 sec)\n",
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"INFO:tensorflow:global_step/sec: 323.497\n",
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"INFO:tensorflow:loss = 0.0074377223, step = 12780 (0.317 sec)\n",
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"INFO:tensorflow:global_step/sec: 318.278\n",
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"INFO:tensorflow:global_step/sec: 280.449\n",
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"INFO:tensorflow:loss = 0.0008953349, step = 20580 (0.364 sec)\n",
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"INFO:tensorflow:global_step/sec: 278.265\n",
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"INFO:tensorflow:loss = 0.0015090622, step = 20680 (0.371 sec)\n"
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]
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},
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{
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:loss = 0.0015090622, step = 20680 (0.371 sec)\n",
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"INFO:tensorflow:global_step/sec: 270.082\n",
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"INFO:tensorflow:loss = 0.0010438098, step = 20780 (0.374 sec)\n",
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"INFO:tensorflow:global_step/sec: 267.97\n",
@@ -2501,7 +2453,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

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