def test_implementation():
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
tests.test_con_pool(conv2d_maxpool)
tests.test_flatten(flatten)
tests.test_fully_conn(fully_conn)
tests.test_output(output)
build_cnn()
tests.test_conv_net(conv_net)
tests.test_train_nn(train_neural_network)
def run_tests():
import problem_unittests as t
t.test_folder_path(cifar10_dataset_folder_path)
t.test_normalize(normalize)
t.test_one_hot_encode(one_hot_encode)
t.test_nn_image_inputs(neural_net_image_input)
t.test_nn_label_inputs(neural_net_label_input)
t.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
t.test_con_pool(conv2conv2d_maxpool)
t.test_flatten(flatten)
t.test_fully_conn(fully_conn)
t.test_output(output)
t.test_conv_net(conv_net)
t.test_train_nn(train_neural_network)
conv = tf.nn.conv2d(x_tensor, weight, [1, conv_strides[0], conv_strides[1], 1], padding = "SAME")
bias = tf.Variable(tf.zeros([conv_num_outputs]))
conv = tf.nn.bias_add(conv, bias)
conv = tf.nn.relu(conv)
conv = tf.nn.max_pool(conv, [1, pool_ksize[0], pool_ksize[1], 1], [1, pool_strides[0], pool_strides[1], 1], padding = "SAME")
return conv
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
# ### Flatten Layer
# Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*).
#
# Shortcut Option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer which help with some high-level features. For more of a challenge, only use other TensorFlow packages.
# In[ ]:
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
mean = 0.0, stddev = 1.0/out_depth))
biases = tf.Variable(tf.zeros(conv_num_outputs))
conv_layer_out = tf.nn.bias_add(tf.nn.conv2d(x_tensor, weights,
strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME'),
biases)
conv_layer_out = tf.nn.relu(conv_layer_out)
maxpooling_out = tf.nn.max_pool(conv_layer_out, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')
return maxpooling_out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
# ### Flatten Layer
# Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.
# In[9]:
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
# TODO: Implement Function
def runConvMaxLayerTest(self):
tests.test_con_pool(conv2d_maxpool)
print("Model ConvMaxLayerTest Ran Successfully")
