class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
-
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
@@ -1277,8 +1276,7 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
model.summary()
@@ -1295,7 +1293,7 @@ model.summary()
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.
Compile and train the model
@@ -1310,7 +1308,6 @@ model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
-
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
@@ -1345,9 +1342,7 @@ plt.xlabel('
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
-
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
-
print(test_acc)
@@ -1416,8 +1411,6 @@ systems such as automatic translation and speech-to-text.
from tensorflow.keras import regularizers
from tensorflow.keras.utils import to_categorical
-
-
# convert into dataset matrix
def convertToMatrix(data, step):
X, Y =[], []
@@ -1457,7 +1450,6 @@ model.add(Dense(.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()
-
model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict= model.predict(testX)
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html
index aacb5cbe2..f91554b01 100644
--- a/doc/pub/week43/html/week43-reveal.html
+++ b/doc/pub/week43/html/week43-reveal.html
@@ -1141,7 +1141,6 @@ train_images, test_images = train_images / 255.0
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
-
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
@@ -1233,8 +1232,7 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
model.summary()
@@ -1251,7 +1249,7 @@ model.summary()
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.
@@ -1267,7 +1265,6 @@ model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
-
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
@@ -1302,9 +1299,7 @@ plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
-
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
-
print(test_acc)
@@ -1375,8 +1370,6 @@ systems such as automatic translation and speech-to-text.
from tensorflow.keras import regularizers
from tensorflow.keras.utils import to_categorical
-
-
# convert into dataset matrix
def convertToMatrix(data, step):
X, Y =[], []
@@ -1416,7 +1409,6 @@ model.add(Dense(8, activation=1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()
-
model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict= model.predict(testX)
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html
index d6e97b7db..c6440f2f5 100644
--- a/doc/pub/week43/html/week43-solarized.html
+++ b/doc/pub/week43/html/week43-solarized.html
@@ -1121,7 +1121,6 @@ train_images, test_images = train_images / 255.0
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
-
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
@@ -1212,8 +1211,7 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
model.summary()
@@ -1230,7 +1228,7 @@ model.summary()
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.
Compile and train the model
@@ -1245,7 +1243,6 @@ model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
-
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
@@ -1280,9 +1277,7 @@ plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
-
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
-
print(test_acc)
@@ -1351,8 +1346,6 @@ systems such as automatic translation and speech-to-text.
from tensorflow.keras import regularizers
from tensorflow.keras.utils import to_categorical
-
-
# convert into dataset matrix
def convertToMatrix(data, step):
X, Y =[], []
@@ -1392,7 +1385,6 @@ model.add(Dense(8, activation=1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()
-
model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict= model.predict(testX)
diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html
index f8717f5d2..4efcd90a5 100644
--- a/doc/pub/week43/html/week43.html
+++ b/doc/pub/week43/html/week43.html
@@ -1198,7 +1198,6 @@ train_images, test_images = train_images class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
-
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
@@ -1289,8 +1288,7 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
model.summary()
@@ -1307,7 +1305,7 @@ model.summary()
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.
Compile and train the model
@@ -1322,7 +1320,6 @@ model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
-
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
@@ -1357,9 +1354,7 @@ plt.xlabel('
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
-
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
-
print(test_acc)
@@ -1428,8 +1423,6 @@ systems such as automatic translation and speech-to-text.
from tensorflow.keras import regularizers
from tensorflow.keras.utils import to_categorical
-
-
# convert into dataset matrix
def convertToMatrix(data, step):
X, Y =[], []
@@ -1469,7 +1462,6 @@ model.add(Dense(.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()
-
model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict= model.predict(testX)
diff --git a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
index 322104dc2..38921dd10 100644
Binary files a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz and b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz differ
diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb
index 8403f4c6c..d8df74187 100644
--- a/doc/pub/week43/ipynb/week43.ipynb
+++ b/doc/pub/week43/ipynb/week43.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "64f0fb95",
+ "id": "c5d39e0b",
"metadata": {
"editable": true
},
@@ -14,7 +14,7 @@
},
{
"cell_type": "markdown",
- "id": "ec4435d5",
+ "id": "6459c579",
"metadata": {
"editable": true
},
@@ -29,7 +29,7 @@
},
{
"cell_type": "markdown",
- "id": "d36232f9",
+ "id": "70ebd784",
"metadata": {
"editable": true
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@@ -59,7 +59,7 @@
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{
"cell_type": "markdown",
- "id": "73938052",
+ "id": "ad740247",
"metadata": {
"editable": true
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@@ -85,7 +85,7 @@
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{
"cell_type": "markdown",
- "id": "230b9055",
+ "id": "9293049f",
"metadata": {
"editable": true
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@@ -112,7 +112,7 @@
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{
"cell_type": "markdown",
- "id": "d15c3dc6",
+ "id": "67a1fb47",
"metadata": {
"editable": true
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@@ -134,7 +134,7 @@
},
{
"cell_type": "markdown",
- "id": "cf6057f4",
+ "id": "5e4646d6",
"metadata": {
"editable": true
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@@ -152,7 +152,7 @@
},
{
"cell_type": "markdown",
- "id": "13df44dc",
+ "id": "9a829df6",
"metadata": {
"editable": true
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@@ -182,7 +182,7 @@
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{
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- "id": "d4317855",
+ "id": "3e53ba7c",
"metadata": {
"editable": true
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@@ -212,7 +212,7 @@
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{
"cell_type": "markdown",
- "id": "edb251e1",
+ "id": "09e08f91",
"metadata": {
"editable": true
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@@ -252,7 +252,7 @@
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{
"cell_type": "markdown",
- "id": "8e0d0bb5",
+ "id": "7c8a5acf",
"metadata": {
"editable": true
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@@ -281,7 +281,7 @@
},
{
"cell_type": "markdown",
- "id": "1045f868",
+ "id": "12071d8a",
"metadata": {
"editable": true
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@@ -303,7 +303,7 @@
},
{
"cell_type": "markdown",
- "id": "989ee7f3",
+ "id": "c1faf1e4",
"metadata": {
"editable": true
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@@ -332,7 +332,7 @@
},
{
"cell_type": "markdown",
- "id": "c9db3ad1",
+ "id": "39016bcb",
"metadata": {
"editable": true
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@@ -349,7 +349,7 @@
},
{
"cell_type": "markdown",
- "id": "0d6f1fa0",
+ "id": "945544d3",
"metadata": {
"editable": true
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@@ -369,7 +369,7 @@
},
{
"cell_type": "markdown",
- "id": "93186e95",
+ "id": "48a2b80c",
"metadata": {
"editable": true
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@@ -381,7 +381,7 @@
},
{
"cell_type": "markdown",
- "id": "3d7ea176",
+ "id": "cfd14636",
"metadata": {
"editable": true
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@@ -393,7 +393,7 @@
},
{
"cell_type": "markdown",
- "id": "96afecaf",
+ "id": "66afa266",
"metadata": {
"editable": true
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@@ -405,7 +405,7 @@
},
{
"cell_type": "markdown",
- "id": "2fca2b28",
+ "id": "96f87f9a",
"metadata": {
"editable": true
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@@ -415,7 +415,7 @@
},
{
"cell_type": "markdown",
- "id": "be464e2e",
+ "id": "f070283e",
"metadata": {
"editable": true
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@@ -427,7 +427,7 @@
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{
"cell_type": "markdown",
- "id": "b65a0439",
+ "id": "9d3db171",
"metadata": {
"editable": true
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@@ -439,7 +439,7 @@
},
{
"cell_type": "markdown",
- "id": "2df4dfd9",
+ "id": "33f6af14",
"metadata": {
"editable": true
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@@ -454,7 +454,7 @@
},
{
"cell_type": "markdown",
- "id": "4a9ba5c1",
+ "id": "02a99cff",
"metadata": {
"editable": true
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@@ -466,7 +466,7 @@
},
{
"cell_type": "markdown",
- "id": "809dc4d2",
+ "id": "2c6a4e0c",
"metadata": {
"editable": true
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@@ -476,7 +476,7 @@
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{
"cell_type": "markdown",
- "id": "5c0ee27d",
+ "id": "08df12bf",
"metadata": {
"editable": true
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@@ -488,7 +488,7 @@
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{
"cell_type": "markdown",
- "id": "1b05fc18",
+ "id": "2d082fe8",
"metadata": {
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@@ -498,7 +498,7 @@
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{
"cell_type": "markdown",
- "id": "c65cb2b7",
+ "id": "19f66253",
"metadata": {
"editable": true
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@@ -510,7 +510,7 @@
},
{
"cell_type": "markdown",
- "id": "99c7c3dc",
+ "id": "06e842d7",
"metadata": {
"editable": true
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@@ -523,7 +523,7 @@
},
{
"cell_type": "markdown",
- "id": "53d53bbc",
+ "id": "60a4a134",
"metadata": {
"editable": true
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@@ -542,7 +542,7 @@
},
{
"cell_type": "markdown",
- "id": "94ac0ef9",
+ "id": "17cb3b7c",
"metadata": {
"editable": true
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@@ -554,7 +554,7 @@
},
{
"cell_type": "markdown",
- "id": "c4f33864",
+ "id": "5c318d22",
"metadata": {
"editable": true
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@@ -566,7 +566,7 @@
},
{
"cell_type": "markdown",
- "id": "92652c28",
+ "id": "35ec45df",
"metadata": {
"editable": true
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@@ -576,7 +576,7 @@
},
{
"cell_type": "markdown",
- "id": "56a16a6a",
+ "id": "2b9c44d7",
"metadata": {
"editable": true
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@@ -588,7 +588,7 @@
},
{
"cell_type": "markdown",
- "id": "92636cff",
+ "id": "70a7d1dc",
"metadata": {
"editable": true
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@@ -598,7 +598,7 @@
},
{
"cell_type": "markdown",
- "id": "560ed487",
+ "id": "a29e240c",
"metadata": {
"editable": true
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@@ -612,7 +612,7 @@
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{
"cell_type": "markdown",
- "id": "2be56ff2",
+ "id": "bf4a8589",
"metadata": {
"editable": true
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@@ -630,7 +630,7 @@
},
{
"cell_type": "markdown",
- "id": "e277d2e6",
+ "id": "325858c8",
"metadata": {
"editable": true
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@@ -641,7 +641,7 @@
},
{
"cell_type": "markdown",
- "id": "44fd182f",
+ "id": "dad45e98",
"metadata": {
"editable": true
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@@ -659,7 +659,7 @@
},
{
"cell_type": "markdown",
- "id": "3e977804",
+ "id": "633095c0",
"metadata": {
"editable": true
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@@ -673,7 +673,7 @@
},
{
"cell_type": "markdown",
- "id": "48348573",
+ "id": "0499b303",
"metadata": {
"editable": true
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@@ -687,7 +687,7 @@
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{
"cell_type": "markdown",
- "id": "bd57bb0b",
+ "id": "9f7145ad",
"metadata": {
"editable": true
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@@ -699,7 +699,7 @@
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{
"cell_type": "markdown",
- "id": "a03db30b",
+ "id": "c8f1867e",
"metadata": {
"editable": true
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@@ -709,7 +709,7 @@
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{
"cell_type": "markdown",
- "id": "621656e3",
+ "id": "c86dd048",
"metadata": {
"editable": true
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@@ -721,7 +721,7 @@
},
{
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- "id": "2a5d79f2",
+ "id": "428656bc",
"metadata": {
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@@ -731,7 +731,7 @@
},
{
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+ "id": "c70e9d1e",
"metadata": {
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@@ -743,7 +743,7 @@
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{
"cell_type": "markdown",
- "id": "58154535",
+ "id": "7b9b0f19",
"metadata": {
"editable": true
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@@ -755,7 +755,7 @@
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{
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- "id": "cb5e7c3b",
+ "id": "ddc85d9b",
"metadata": {
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@@ -782,7 +782,7 @@
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{
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- "id": "2c679c06",
+ "id": "004f94f5",
"metadata": {
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@@ -794,7 +794,7 @@
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{
"cell_type": "markdown",
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+ "id": "ecbfa56d",
"metadata": {
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@@ -804,7 +804,7 @@
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{
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- "id": "fb8f30dc",
+ "id": "ac1647b2",
"metadata": {
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@@ -830,7 +830,7 @@
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{
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+ "id": "d43e606a",
"metadata": {
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@@ -842,7 +842,7 @@
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{
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+ "id": "8cce1a2a",
"metadata": {
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@@ -861,7 +861,7 @@
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{
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+ "id": "c44045c5",
"metadata": {
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@@ -874,7 +874,7 @@
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{
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+ "id": "59a365da",
"metadata": {
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@@ -886,7 +886,7 @@
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{
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+ "id": "34ae8a74",
"metadata": {
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@@ -907,7 +907,7 @@
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{
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+ "id": "c67263a9",
"metadata": {
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@@ -928,7 +928,7 @@
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{
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+ "id": "795378a9",
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@@ -953,7 +953,7 @@
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@@ -972,7 +972,7 @@
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@@ -983,7 +983,7 @@
{
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- "id": "f92b952c",
+ "id": "f8a921f2",
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"editable": true
@@ -1036,7 +1036,7 @@
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{
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"metadata": {
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@@ -1047,7 +1047,7 @@
{
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- "id": "968f01ec",
+ "id": "615d9546",
"metadata": {
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@@ -1080,7 +1080,7 @@
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{
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+ "id": "878a352e",
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@@ -1091,7 +1091,7 @@
{
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"execution_count": 3,
- "id": "0e81b3b9",
+ "id": "05878dc8",
"metadata": {
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@@ -1128,7 +1128,7 @@
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{
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- "id": "a180bb4f",
+ "id": "538e069f",
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@@ -1139,7 +1139,7 @@
{
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- "id": "c32d897b",
+ "id": "4c91e0c7",
"metadata": {
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@@ -1166,7 +1166,7 @@
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{
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- "id": "4d53cf65",
+ "id": "4708fdf8",
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@@ -1177,7 +1177,7 @@
{
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- "id": "0f1e5f83",
+ "id": "dc871d8f",
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@@ -1218,7 +1218,7 @@
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"cell_type": "markdown",
- "id": "e6468862",
+ "id": "4cc87ad5",
"metadata": {
"editable": true
},
@@ -1234,7 +1234,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "58aaf533",
+ "id": "6a075034",
"metadata": {
"collapsed": false,
"editable": true
@@ -1255,7 +1255,7 @@
},
{
"cell_type": "markdown",
- "id": "d8f52e33",
+ "id": "b48d3436",
"metadata": {
"editable": true
},
@@ -1268,7 +1268,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "afd58fba",
+ "id": "82256673",
"metadata": {
"collapsed": false,
"editable": true
@@ -1277,7 +1277,6 @@
"source": [
"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
- "\n",
"plt.figure(figsize=(10,10))\n",
"for i in range(25):\n",
" plt.subplot(5,5,i+1)\n",
@@ -1293,7 +1292,7 @@
},
{
"cell_type": "markdown",
- "id": "26008a78",
+ "id": "2de5f864",
"metadata": {
"editable": true
},
@@ -1308,7 +1307,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "fdf773da",
+ "id": "34cd91dc",
"metadata": {
"collapsed": false,
"editable": true
@@ -1329,7 +1328,7 @@
},
{
"cell_type": "markdown",
- "id": "9ebc2edb",
+ "id": "c51270ac",
"metadata": {
"editable": true
},
@@ -1339,7 +1338,7 @@
},
{
"cell_type": "markdown",
- "id": "3b98a7e4",
+ "id": "f7680dfc",
"metadata": {
"editable": true
},
@@ -1358,7 +1357,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "3bfb8db3",
+ "id": "b923f7c4",
"metadata": {
"collapsed": false,
"editable": true
@@ -1368,24 +1367,23 @@
"model.add(layers.Flatten())\n",
"model.add(layers.Dense(64, activation='relu'))\n",
"model.add(layers.Dense(10))\n",
- "Here's the complete architecture of our model.\n",
- "\n",
+ "#Here's the complete architecture of our model.\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
- "id": "ceb8c2c4",
+ "id": "8107d910",
"metadata": {
"editable": true
},
"source": [
- "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers."
+ "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers."
]
},
{
"cell_type": "markdown",
- "id": "f762fe86",
+ "id": "d6436c68",
"metadata": {
"editable": true
},
@@ -1396,7 +1394,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "c088fa80",
+ "id": "6b1d461a",
"metadata": {
"collapsed": false,
"editable": true
@@ -1406,14 +1404,13 @@
"model.compile(optimizer='adam',\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['accuracy'])\n",
- "\n",
"history = model.fit(train_images, train_labels, epochs=10, \n",
" validation_data=(test_images, test_labels))"
]
},
{
"cell_type": "markdown",
- "id": "6a58f2f9",
+ "id": "a9853275",
"metadata": {
"editable": true
},
@@ -1424,7 +1421,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "970b93c1",
+ "id": "3641ff80",
"metadata": {
"collapsed": false,
"editable": true
@@ -1437,15 +1434,13 @@
"plt.ylabel('Accuracy')\n",
"plt.ylim([0.5, 1])\n",
"plt.legend(loc='lower right')\n",
- "\n",
"test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
- "\n",
"print(test_acc)"
]
},
{
"cell_type": "markdown",
- "id": "595bbe49",
+ "id": "4e8bb911",
"metadata": {
"editable": true
},
@@ -1472,7 +1467,7 @@
},
{
"cell_type": "markdown",
- "id": "a6a3ceed",
+ "id": "d10dd84c",
"metadata": {
"editable": true
},
@@ -1484,7 +1479,7 @@
},
{
"cell_type": "markdown",
- "id": "1c11cfe0",
+ "id": "6791aa19",
"metadata": {
"editable": true
},
@@ -1495,7 +1490,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "3c9c960e",
+ "id": "1a9bea34",
"metadata": {
"collapsed": false,
"editable": true
@@ -1515,8 +1510,6 @@
"from tensorflow.keras import regularizers \n",
"from tensorflow.keras.utils import to_categorical \n",
"\n",
- "\n",
- "\n",
"# convert into dataset matrix\n",
"def convertToMatrix(data, step):\n",
" X, Y =[], []\n",
@@ -1556,7 +1549,6 @@
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='rmsprop')\n",
"model.summary()\n",
- "\n",
"model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)\n",
"trainPredict = model.predict(trainX)\n",
"testPredict= model.predict(testX)\n",
@@ -1574,7 +1566,7 @@
},
{
"cell_type": "markdown",
- "id": "3ead3155",
+ "id": "7e222e09",
"metadata": {
"editable": true
},
@@ -1590,7 +1582,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "ccae4f21",
+ "id": "fa1b4b28",
"metadata": {
"collapsed": false,
"editable": true
@@ -1629,7 +1621,7 @@
},
{
"cell_type": "markdown",
- "id": "7d6b63a0",
+ "id": "c6237960",
"metadata": {
"editable": true
},
@@ -1673,7 +1665,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "671b8076",
+ "id": "777176d4",
"metadata": {
"collapsed": false,
"editable": true
@@ -1756,7 +1748,7 @@
},
{
"cell_type": "markdown",
- "id": "9b7b896b",
+ "id": "e5b3cd18",
"metadata": {
"editable": true
},
@@ -1767,7 +1759,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "33bb81ea",
+ "id": "8bd0ef49",
"metadata": {
"collapsed": false,
"editable": true
@@ -1869,7 +1861,7 @@
},
{
"cell_type": "markdown",
- "id": "421951bd",
+ "id": "ca50ecee",
"metadata": {
"editable": true
},
@@ -1891,7 +1883,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "cce1b4fc",
+ "id": "0f56a065",
"metadata": {
"collapsed": false,
"editable": true
@@ -1988,7 +1980,7 @@
},
{
"cell_type": "markdown",
- "id": "b1eac503",
+ "id": "231486ad",
"metadata": {
"editable": true
},
@@ -2014,7 +2006,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "54144082",
+ "id": "346c01ec",
"metadata": {
"collapsed": false,
"editable": true
@@ -2215,7 +2207,7 @@
},
{
"cell_type": "markdown",
- "id": "102d34c5",
+ "id": "1e855715",
"metadata": {
"editable": true
},
@@ -2239,7 +2231,7 @@
},
{
"cell_type": "markdown",
- "id": "850021bc",
+ "id": "74c691f8",
"metadata": {
"editable": true
},
@@ -2259,7 +2251,7 @@
},
{
"cell_type": "markdown",
- "id": "17b9ceac",
+ "id": "20b0f4e4",
"metadata": {
"editable": true
},
@@ -2277,7 +2269,7 @@
},
{
"cell_type": "markdown",
- "id": "356c6b8d",
+ "id": "60295fd3",
"metadata": {
"editable": true
},
@@ -2292,7 +2284,7 @@
},
{
"cell_type": "markdown",
- "id": "3ac797c4",
+ "id": "06f276d9",
"metadata": {
"editable": true
},
@@ -2310,7 +2302,7 @@
},
{
"cell_type": "markdown",
- "id": "234ecc59",
+ "id": "ac42c4db",
"metadata": {
"editable": true
},
@@ -2323,7 +2315,7 @@
},
{
"cell_type": "markdown",
- "id": "aa46be2b",
+ "id": "04c1b0fe",
"metadata": {
"editable": true
},
@@ -2341,7 +2333,7 @@
},
{
"cell_type": "markdown",
- "id": "916b05b5",
+ "id": "91abaad4",
"metadata": {
"editable": true
},
@@ -2352,7 +2344,7 @@
},
{
"cell_type": "markdown",
- "id": "f1596b0b",
+ "id": "18714b71",
"metadata": {
"editable": true
},
@@ -2370,7 +2362,7 @@
},
{
"cell_type": "markdown",
- "id": "29c9cda8",
+ "id": "6b83109a",
"metadata": {
"editable": true
},
@@ -2397,7 +2389,7 @@
},
{
"cell_type": "markdown",
- "id": "0d0f4f2a",
+ "id": "f4623825",
"metadata": {
"editable": true
},
@@ -2409,7 +2401,7 @@
},
{
"cell_type": "markdown",
- "id": "26ad50df",
+ "id": "97e20104",
"metadata": {
"editable": true
},
@@ -2428,7 +2420,7 @@
},
{
"cell_type": "markdown",
- "id": "28576f76",
+ "id": "a11dc809",
"metadata": {
"editable": true
},
@@ -2438,7 +2430,7 @@
},
{
"cell_type": "markdown",
- "id": "69f18cef",
+ "id": "4e1c6fc8",
"metadata": {
"editable": true
},
@@ -2458,7 +2450,7 @@
},
{
"cell_type": "markdown",
- "id": "e98fe7bd",
+ "id": "6f811ebb",
"metadata": {
"editable": true
},
@@ -2470,7 +2462,7 @@
},
{
"cell_type": "markdown",
- "id": "33625620",
+ "id": "609dbebf",
"metadata": {
"editable": true
},
@@ -2488,7 +2480,7 @@
},
{
"cell_type": "markdown",
- "id": "03ade079",
+ "id": "b8e9dc97",
"metadata": {
"editable": true
},
@@ -2500,7 +2492,7 @@
},
{
"cell_type": "markdown",
- "id": "41f30d1c",
+ "id": "50adaf2b",
"metadata": {
"editable": true
},
@@ -2521,7 +2513,7 @@
},
{
"cell_type": "markdown",
- "id": "e62959d8",
+ "id": "f7f89a95",
"metadata": {
"editable": true
},
@@ -2538,7 +2530,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "1c0fe176",
+ "id": "e9dae945",
"metadata": {
"collapsed": false,
"editable": true
@@ -2556,7 +2548,7 @@
},
{
"cell_type": "markdown",
- "id": "e059945f",
+ "id": "8469c1ae",
"metadata": {
"editable": true
},
@@ -2567,7 +2559,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "b46c3171",
+ "id": "4f06f394",
"metadata": {
"collapsed": false,
"editable": true
@@ -2593,7 +2585,7 @@
},
{
"cell_type": "markdown",
- "id": "eda283cb",
+ "id": "9c5cd3cf",
"metadata": {
"editable": true
},
@@ -2606,7 +2598,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "e7be1c2e",
+ "id": "db7ee861",
"metadata": {
"collapsed": false,
"editable": true
@@ -2619,7 +2611,7 @@
},
{
"cell_type": "markdown",
- "id": "6a3e4f06",
+ "id": "290ed024",
"metadata": {
"editable": true
},
@@ -2638,7 +2630,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "aaf9a85c",
+ "id": "86f144d3",
"metadata": {
"collapsed": false,
"editable": true
@@ -2706,7 +2698,7 @@
},
{
"cell_type": "markdown",
- "id": "05051929",
+ "id": "8bf7ab88",
"metadata": {
"editable": true
},
@@ -2719,7 +2711,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "47dfdf75",
+ "id": "ee8291f7",
"metadata": {
"collapsed": false,
"editable": true
@@ -2759,7 +2751,7 @@
},
{
"cell_type": "markdown",
- "id": "4e4c9b20",
+ "id": "fffb3d71",
"metadata": {
"editable": true
},
@@ -2771,7 +2763,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "1c73bbbb",
+ "id": "a78aeff3",
"metadata": {
"collapsed": false,
"editable": true
@@ -2785,7 +2777,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "cb263f7b",
+ "id": "b6841971",
"metadata": {
"collapsed": false,
"editable": true
@@ -2798,7 +2790,7 @@
},
{
"cell_type": "markdown",
- "id": "594a6faa",
+ "id": "7a0e935e",
"metadata": {
"editable": true
},
@@ -2809,7 +2801,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "b4bf2541",
+ "id": "4d955ef1",
"metadata": {
"collapsed": false,
"editable": true
@@ -2823,7 +2815,7 @@
},
{
"cell_type": "markdown",
- "id": "30f5f988",
+ "id": "1f02c4d5",
"metadata": {
"editable": true
},
@@ -2838,7 +2830,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "0057bc45",
+ "id": "4f2c7e3b",
"metadata": {
"collapsed": false,
"editable": true
@@ -2854,7 +2846,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "b7d13cf5",
+ "id": "3e283626",
"metadata": {
"collapsed": false,
"editable": true
@@ -2871,7 +2863,7 @@
},
{
"cell_type": "markdown",
- "id": "b66ef096",
+ "id": "f24a66e8",
"metadata": {
"editable": true
},
@@ -2883,7 +2875,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "265edec4",
+ "id": "fe431bbc",
"metadata": {
"collapsed": false,
"editable": true
@@ -2897,7 +2889,7 @@
},
{
"cell_type": "markdown",
- "id": "d8b925a9",
+ "id": "08bedbc8",
"metadata": {
"editable": true
},
@@ -2913,7 +2905,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "4c24e07c",
+ "id": "dcbbfe02",
"metadata": {
"collapsed": false,
"editable": true
@@ -2947,7 +2939,7 @@
},
{
"cell_type": "markdown",
- "id": "226e11aa",
+ "id": "642f178c",
"metadata": {
"editable": true
},
@@ -2960,7 +2952,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "12665849",
+ "id": "8eb8f078",
"metadata": {
"collapsed": false,
"editable": true
@@ -2985,7 +2977,7 @@
},
{
"cell_type": "markdown",
- "id": "6dbda3fc",
+ "id": "185100ba",
"metadata": {
"editable": true
},
@@ -2999,7 +2991,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "f8e512f6",
+ "id": "6a256e74",
"metadata": {
"collapsed": false,
"editable": true
@@ -3017,7 +3009,7 @@
},
{
"cell_type": "markdown",
- "id": "22d5f913",
+ "id": "bd97b51d",
"metadata": {
"editable": true
},
@@ -3028,7 +3020,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "id": "820c1dc6",
+ "id": "42ddd910",
"metadata": {
"collapsed": false,
"editable": true
@@ -3068,7 +3060,7 @@
},
{
"cell_type": "markdown",
- "id": "364f2202",
+ "id": "cab3288a",
"metadata": {
"editable": true
},
@@ -3080,7 +3072,7 @@
{
"cell_type": "code",
"execution_count": 33,
- "id": "edcb5cf6",
+ "id": "e3e4e46b",
"metadata": {
"collapsed": false,
"editable": true
@@ -3092,7 +3084,7 @@
},
{
"cell_type": "markdown",
- "id": "0a69b6db",
+ "id": "fa628eb4",
"metadata": {
"editable": true
},
@@ -3106,7 +3098,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "id": "e62ec4b9",
+ "id": "a29d7a36",
"metadata": {
"collapsed": false,
"editable": true
@@ -3123,7 +3115,7 @@
},
{
"cell_type": "markdown",
- "id": "30719441",
+ "id": "c97bb950",
"metadata": {
"editable": true
},
@@ -3137,7 +3129,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "id": "fdd86d94",
+ "id": "75c02c4f",
"metadata": {
"collapsed": false,
"editable": true
@@ -3154,7 +3146,7 @@
},
{
"cell_type": "markdown",
- "id": "e650dcb0",
+ "id": "7002de8e",
"metadata": {
"editable": true
},
@@ -3170,7 +3162,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "c10ede25",
+ "id": "7fafeb29",
"metadata": {
"collapsed": false,
"editable": true
@@ -3198,7 +3190,7 @@
{
"cell_type": "code",
"execution_count": 37,
- "id": "007f5c1c",
+ "id": "965eee54",
"metadata": {
"collapsed": false,
"editable": true
@@ -3221,7 +3213,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "id": "2e4e23bf",
+ "id": "08a29dab",
"metadata": {
"collapsed": false,
"editable": true
@@ -3234,7 +3226,7 @@
},
{
"cell_type": "markdown",
- "id": "0d6229f1",
+ "id": "889fcecb",
"metadata": {
"editable": true
},
@@ -3250,7 +3242,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "id": "331eb2c0",
+ "id": "437bc288",
"metadata": {
"collapsed": false,
"editable": true
@@ -3277,7 +3269,7 @@
},
{
"cell_type": "markdown",
- "id": "f40a0233",
+ "id": "ccbd2be7",
"metadata": {
"editable": true
},
@@ -3292,7 +3284,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "id": "e7532cee",
+ "id": "61d645f2",
"metadata": {
"collapsed": false,
"editable": true
@@ -3308,7 +3300,7 @@
},
{
"cell_type": "markdown",
- "id": "50d2c1af",
+ "id": "e098db36",
"metadata": {
"editable": true
},
@@ -3319,7 +3311,7 @@
},
{
"cell_type": "markdown",
- "id": "1bb36160",
+ "id": "94429655",
"metadata": {
"editable": true
},
@@ -3337,7 +3329,7 @@
{
"cell_type": "code",
"execution_count": 41,
- "id": "94ff439b",
+ "id": "2af9e60d",
"metadata": {
"collapsed": false,
"editable": true
@@ -3355,7 +3347,7 @@
},
{
"cell_type": "markdown",
- "id": "9ea83687",
+ "id": "26be8697",
"metadata": {
"editable": true
},
@@ -3366,7 +3358,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "id": "8a4ae113",
+ "id": "b6534afb",
"metadata": {
"collapsed": false,
"editable": true
diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt
index a5574a954..1d68fa871 100644
--- a/doc/src/week43/week43.do.txt
+++ b/doc/src/week43/week43.do.txt
@@ -745,7 +745,6 @@ To verify that the dataset looks correct, let's plot the first 25 images from th
!bc pycod
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
-
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
@@ -799,11 +798,10 @@ layer with 10 outputs and a softmax activation.
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
model.summary()
!ec
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.
!split
===== Compile and train the model =====
@@ -812,7 +810,6 @@ As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (102
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
-
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
@@ -829,11 +826,8 @@ plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
-
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
-
print(test_acc)
-
!ec
@@ -880,8 +874,6 @@ from tensorflow.keras import optimizers
from tensorflow.keras import regularizers
from tensorflow.keras.utils import to_categorical
-
-
# convert into dataset matrix
def convertToMatrix(data, step):
X, Y =[], []
@@ -921,7 +913,6 @@ model.add(Dense(8, activation="relu"))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()
-
model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict= model.predict(testX)