model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(num_classes, activation='softmax'))
# In[14]:
# preprocess data
X_normalized = np.array(X_train / 255.0 - 0.5)
from sklearn.preprocessing import LabelBinarizer
label_binarizer = LabelBinarizer()
y_one_hot = label_binarizer.fit_transform(y_train)
model.compile('adam', 'categorical_crossentropy', ['accuracy'])
# TODO: change the number of training epochs to 3
history = model.fit(X_normalized, y_one_hot, epochs=3, validation_split=0.2)
# In[15]:
### DON'T MODIFY ANYTHING BELOW ###
### Be sure to run all cells above before running this cell ###
import grader
try:
grader.run_grader(model, history)
except Exception as err:
print(str(err))
# In[ ]:
# Weights and biases
weights = [tf.Variable(hidden_layer_weights), tf.Variable(out_weights)]
biases = [tf.Variable(tf.zeros(3)), tf.Variable(tf.zeros(2))]
# Input
features = tf.Variable([[0.0, 2.0, 3.0, 4.0], [0.1, 0.2, 0.3, 0.4],
[11.0, 12.0, 13.0, 14.0]])
# TODO: Create Model with Dropout
keep_prob = tf.placeholder(tf.float32)
hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)
hidden_layer = tf.nn.dropout(hidden_layer, keep_prob)
logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])
# TODO: save and print session results as variable named "output"
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, feed_dict={keep_prob: 0.5})
print(output)
### DON'T MODIFY ANYTHING BELOW ###
### Be sure to run all cells above before running this cell ###
import grader
try:
grader.run_grader(output)
except Exception as err:
print(str(err))
# TODO: Set the stride for each dimension (batch_size, height, width, depth)
strides = [?, ?, ?, ?]
# TODO: set the padding, either 'VALID' or 'SAME'.
padding = ?
# https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#conv2d
# `tf.nn.conv2d` does not include the bias computation so we have to add it ourselves after.
return tf.nn.conv2d(input_array, F_W, strides, padding) + F_b
output = conv2d(X)
output
# In[ ]:
##### Do Not Modify ######
import grader
test_X = tf.constant(np.random.randn(1, 4, 4, 1), dtype=tf.float32)
try:
response = grader.run_grader(test_X, conv2d)
print(response)
except Exception as err:
print(str(err))
[1, 0], # go down
[0, 1]
] # go right
delta_name = ['^', '<', 'v', '>']
def search(grid, init, goal, cost):
# ----------------------------------------
# insert code here
# ----------------------------------------
return path
##### Do Not Modify ######
import grader
try:
response = grader.run_grader(search)
print(response)
except Exception as err:
print(str(err))
##### SOLUTION: Run this cell to watch the solution video ######
from IPython.display import HTML
HTML(
'<iframe width="560" height="315" src="https://www.youtube.com/embed/cl8Kdkr4Gbg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
)
# Solution is available in the other "solution.ipynb"
import tensorflow as tf
def run():
output = None
logit_data = [2.0, 1.0, 0.1]
logits = tf.placeholder(tf.float32)
# TODO: Calculate the softmax of the logits
softmax = tf.nn.softmax(logits)
with tf.Session() as sess:
output = sess.run(softmax, feed_dict={logits: logit_data})
return output
### DON'T MODIFY ANYTHING BELOW ###
### Be sure to run all cells above before running this cell ###
import grader
try:
grader.run_grader(run)
except Exception as err:
print(str(err))
# Training loss
# You'll learn more about this in future lessons.
loss = tf.reduce_mean(cross_entropy)
# Rate at which the weights are changed
# You'll learn more about this in future lessons.
learning_rate = 0.08
# Gradient Descent
# This is the method used to train the model
# You'll learn more about this in future lessons.
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
# Run optimizer and get loss
_, l = session.run(
[optimizer, loss],
feed_dict={features: train_features, labels: train_labels})
# Print loss
print('Loss: {}'.format(l))
### DON'T MODIFY ANYTHING BELOW ###
### Be sure to run all cells above before running this cell ###
import grader
try:
grader.run_grader(get_weights, get_biases, linear)
except Exception as err:
print(str(err))
with open('small_test_traffic.p', 'rb') as f:
data_test = pickle.load(f)
X_test = data_test['features']
y_test = data_test['labels']
# preprocess data
X_normalized_test = np.array(X_test / 255.0 - 0.5)
y_one_hot_test = label_binarizer.fit_transform(y_test)
print("Testing")
metrics = model.evaluate(X_normalized_test, y_one_hot_test)
for metric_i in range(len(model.metrics_names)):
metric_name = model.metrics_names[metric_i]
metric_value = metrics[metric_i]
print('{}: {}'.format(metric_name, metric_value))
# In[16]:
### DON'T MODIFY ANYTHING BELOW ###
### Be sure to run all cells above before running this cell ###
import grader
try:
grader.run_grader(metrics)
except Exception as err:
print(str(err))
# In[ ]:
