Lab 10.9.3: Convolutional Neural Networks#
Attribution#
This notebook follows lab 10.9.3 from ISLRv2. The R-code has been ported to Python by Daniel Kapitan (08-01-2022).
Data preparation#
In this section we fit a CNN to the CIFAR100 data, which is available in the Keras package. It is arranged in a similar fashion as the MNIST data.
import os
import pickle
from pprint import pprint
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
import altair as alt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from tensorflow.keras.datasets import cifar100
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
%matplotlib inline
# load the data
(X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode='fine')
print("Train matrix shape", X_train.shape)
print("Test matrix shape", X_test.shape)
print(f"Range of values train matrix: {X_train.min()} ... {X_train.max()}")
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
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Train matrix shape (50000, 32, 32, 3)
Test matrix shape (10000, 32, 32, 3)
Range of values train matrix: 0 ... 255
The array of 50,000 training images has four dimesnions: each three-color image is represented as a set of three channels, each of which consists of \(32 \times 32\) eight-bit pixels. We standardize as we dit for the digits, but keep the array structure. We one-hot encode the response factors to produce a 100-column binary matrix.
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
# one-hot encoding using keras' numpy-related utilities
n_classes = 100
print("Shape before one-hot encoding: ", y_train.shape)
Y_train = to_categorical(y_train, n_classes)
Y_test = to_categorical(y_test, n_classes)
print("Shape after one-hot encoding: ", Y_train.shape)
Shape before one-hot encoding: (50000, 1)
Shape after one-hot encoding: (50000, 100)
Before we start, we look at some of the training images. The file cifar100_meta contains the label names.
fig = plt.figure()
for i in range(3):
plt.subplot(1,3,i+1)
plt.imshow(X_train[i])
plt.title(f"{labels['fine_label_names'][y_train[i][0]]}")
fig;
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[3], line 5
3 plt.subplot(1,3,i+1)
4 plt.imshow(X_train[i])
----> 5 plt.title(f"{labels['fine_label_names'][y_train[i][0]]}")
6 fig;
NameError: name 'labels' is not defined
Building the network#
Here we specify a moderately-sized CNN for demonstration purposes.
# building a linear stack of layers with the sequential model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape= (32, 32, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(100, activation='softmax'))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 32) 896
max_pooling2d (MaxPooling2 (None, 16, 16, 32) 0
D)
conv2d_1 (Conv2D) (None, 16, 16, 64) 18496
max_pooling2d_1 (MaxPoolin (None, 8, 8, 64) 0
g2D)
conv2d_2 (Conv2D) (None, 8, 8, 128) 73856
max_pooling2d_2 (MaxPoolin (None, 4, 4, 128) 0
g2D)
conv2d_3 (Conv2D) (None, 4, 4, 256) 295168
max_pooling2d_3 (MaxPoolin (None, 2, 2, 256) 0
g2D)
flatten (Flatten) (None, 1024) 0
dropout (Dropout) (None, 1024) 0
dense (Dense) (None, 512) 524800
dense_1 (Dense) (None, 100) 51300
=================================================================
Total params: 964516 (3.68 MB)
Trainable params: 964516 (3.68 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
Notice that we used the padding = same argument to Conv2D(), which ensures that the output channels have the same dimension as the input channels. There are 32 channels in the first hidden layer, in contrast to the three channels in the input layer. We use a \(3 \times 3\) convolution filter for each channel in all the layers. Each convolution is followed by a max-pooling layer over \(2 \times 2\) blocks. By studying the summary, we can see that the channels halve in both dimensions after each of the max-pooling operations. After the last of these we have a layer with 256 channels of dimension \(2 \times 2\). These are then flattened to a dense layer of size 1,024: in other words, each of the \(2 \times 2\) matrics is turned into a 4-vector, and put side-by-side in one layer. This is followed by a dropout regularization layer, then another dense layers of size 512, which finally reaches the softmax output layer.
Compiling and training the Model#
Finally, we specify the fitting algorithm, and fit the model.
%%time
# compiling the sequential model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
# training the model and saving metrics in history
history = model.fit(X_train, Y_train,
batch_size=128, epochs=30, validation_split=0.2)
# save the model
model_name = 'keras_cifar100.h5'
model.save(model_name)
print(f'Saved trained model at {model_name}')
Epoch 1/30
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313/313 [==============================] - 42s 132ms/step - loss: 4.1064 - accuracy: 0.0659 - val_loss: 3.6454 - val_accuracy: 0.1336
Epoch 2/30
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313/313 [==============================] - 41s 130ms/step - loss: 3.4509 - accuracy: 0.1688 - val_loss: 3.2378 - val_accuracy: 0.2115
Epoch 3/30
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313/313 [==============================] - 41s 130ms/step - loss: 3.0817 - accuracy: 0.2364 - val_loss: 2.9259 - val_accuracy: 0.2724
Epoch 4/30
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313/313 [==============================] - ETA: 0s - loss: 2.8362 - accuracy: 0.2855
313/313 [==============================] - 41s 131ms/step - loss: 2.8362 - accuracy: 0.2855 - val_loss: 2.7162 - val_accuracy: 0.3175
Epoch 5/30
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313/313 [==============================] - 41s 130ms/step - loss: 2.6459 - accuracy: 0.3239 - val_loss: 2.6036 - val_accuracy: 0.3367
Epoch 6/30
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313/313 [==============================] - 41s 131ms/step - loss: 2.4890 - accuracy: 0.3569 - val_loss: 2.5075 - val_accuracy: 0.3663
Epoch 7/30
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313/313 [==============================] - 41s 131ms/step - loss: 2.3388 - accuracy: 0.3846 - val_loss: 2.4799 - val_accuracy: 0.3657
Epoch 8/30
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313/313 [==============================] - 41s 130ms/step - loss: 2.2099 - accuracy: 0.4144 - val_loss: 2.3584 - val_accuracy: 0.3971
Epoch 9/30
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313/313 [==============================] - 41s 131ms/step - loss: 2.0939 - accuracy: 0.4401 - val_loss: 2.2886 - val_accuracy: 0.4085
Epoch 10/30
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313/313 [==============================] - 41s 131ms/step - loss: 1.9828 - accuracy: 0.4611 - val_loss: 2.3140 - val_accuracy: 0.4077
Epoch 11/30
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313/313 [==============================] - 41s 131ms/step - loss: 1.8804 - accuracy: 0.4857 - val_loss: 2.2557 - val_accuracy: 0.4220
Epoch 12/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.7992 - accuracy: 0.5030 - val_loss: 2.2546 - val_accuracy: 0.4191
Epoch 13/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.7101 - accuracy: 0.5211 - val_loss: 2.2178 - val_accuracy: 0.4321
Epoch 14/30
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Epoch 15/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.5453 - accuracy: 0.5577 - val_loss: 2.2504 - val_accuracy: 0.4305
Epoch 16/30
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313/313 [==============================] - 41s 131ms/step - loss: 1.4814 - accuracy: 0.5767 - val_loss: 2.2809 - val_accuracy: 0.4282
Epoch 17/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.4196 - accuracy: 0.5872 - val_loss: 2.2403 - val_accuracy: 0.4342
Epoch 18/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.3535 - accuracy: 0.6047 - val_loss: 2.2384 - val_accuracy: 0.4419
Epoch 19/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.3001 - accuracy: 0.6182 - val_loss: 2.2696 - val_accuracy: 0.4393
Epoch 20/30
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313/313 [==============================] - 41s 131ms/step - loss: 1.2433 - accuracy: 0.6327 - val_loss: 2.2647 - val_accuracy: 0.4404
Epoch 21/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.1960 - accuracy: 0.6473 - val_loss: 2.3030 - val_accuracy: 0.4448
Epoch 22/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.1512 - accuracy: 0.6532 - val_loss: 2.3009 - val_accuracy: 0.4330
Epoch 23/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.1028 - accuracy: 0.6666 - val_loss: 2.3110 - val_accuracy: 0.4380
Epoch 24/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.0635 - accuracy: 0.6784 - val_loss: 2.3345 - val_accuracy: 0.4423
Epoch 25/30
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313/313 [==============================] - 41s 130ms/step - loss: 1.0224 - accuracy: 0.6883 - val_loss: 2.3679 - val_accuracy: 0.4449
Epoch 26/30
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313/313 [==============================] - 41s 130ms/step - loss: 0.9990 - accuracy: 0.6955 - val_loss: 2.4214 - val_accuracy: 0.4471
Epoch 27/30
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313/313 [==============================] - 41s 130ms/step - loss: 0.9639 - accuracy: 0.7056 - val_loss: 2.4197 - val_accuracy: 0.4448
Epoch 28/30
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313/313 [==============================] - 41s 130ms/step - loss: 0.9350 - accuracy: 0.7119 - val_loss: 2.4945 - val_accuracy: 0.4419
Epoch 29/30
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313/313 [==============================] - 41s 130ms/step - loss: 0.9016 - accuracy: 0.7235 - val_loss: 2.4846 - val_accuracy: 0.4489
Epoch 30/30
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---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
File <timed exec>:5
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py:65, in filter_traceback.<locals>.error_handler(*args, **kwargs)
63 filtered_tb = None
64 try:
---> 65 return fn(*args, **kwargs)
66 except Exception as e:
67 filtered_tb = _process_traceback_frames(e.__traceback__)
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/keras/src/engine/training.py:1742, in Model.fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1734 with tf.profiler.experimental.Trace(
1735 "train",
1736 epoch_num=epoch,
(...)
1739 _r=1,
1740 ):
1741 callbacks.on_train_batch_begin(step)
-> 1742 tmp_logs = self.train_function(iterator)
1743 if data_handler.should_sync:
1744 context.async_wait()
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150, in filter_traceback.<locals>.error_handler(*args, **kwargs)
148 filtered_tb = None
149 try:
--> 150 return fn(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.__traceback__)
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825, in Function.__call__(self, *args, **kwds)
822 compiler = "xla" if self._jit_compile else "nonXla"
824 with OptionalXlaContext(self._jit_compile):
--> 825 result = self._call(*args, **kwds)
827 new_tracing_count = self.experimental_get_tracing_count()
828 without_tracing = (tracing_count == new_tracing_count)
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:857, in Function._call(self, *args, **kwds)
854 self._lock.release()
855 # In this case we have created variables on the first call, so we run the
856 # defunned version which is guaranteed to never create variables.
--> 857 return self._no_variable_creation_fn(*args, **kwds) # pylint: disable=not-callable
858 elif self._variable_creation_fn is not None:
859 # Release the lock early so that multiple threads can perform the call
860 # in parallel.
861 self._lock.release()
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148, in TracingCompiler.__call__(self, *args, **kwargs)
145 with self._lock:
146 (concrete_function,
147 filtered_flat_args) = self._maybe_define_function(args, kwargs)
--> 148 return concrete_function._call_flat(
149 filtered_flat_args, captured_inputs=concrete_function.captured_inputs)
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349, in ConcreteFunction._call_flat(self, args, captured_inputs)
1345 possible_gradient_type = gradients_util.PossibleTapeGradientTypes(args)
1346 if (possible_gradient_type == gradients_util.POSSIBLE_GRADIENT_TYPES_NONE
1347 and executing_eagerly):
1348 # No tape is watching; skip to running the function.
-> 1349 return self._build_call_outputs(self._inference_function(*args))
1350 forward_backward = self._select_forward_and_backward_functions(
1351 args,
1352 possible_gradient_type,
1353 executing_eagerly)
1354 forward_function, args_with_tangents = forward_backward.forward()
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196, in AtomicFunction.__call__(self, *args)
194 with record.stop_recording():
195 if self._bound_context.executing_eagerly():
--> 196 outputs = self._bound_context.call_function(
197 self.name,
198 list(args),
199 len(self.function_type.flat_outputs),
200 )
201 else:
202 outputs = make_call_op_in_graph(self, list(args))
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/context.py:1457, in Context.call_function(self, name, tensor_inputs, num_outputs)
1455 cancellation_context = cancellation.context()
1456 if cancellation_context is None:
-> 1457 outputs = execute.execute(
1458 name.decode("utf-8"),
1459 num_outputs=num_outputs,
1460 inputs=tensor_inputs,
1461 attrs=attrs,
1462 ctx=self,
1463 )
1464 else:
1465 outputs = execute.execute_with_cancellation(
1466 name.decode("utf-8"),
1467 num_outputs=num_outputs,
(...)
1471 cancellation_manager=cancellation_context,
1472 )
File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:
KeyboardInterrupt:
# plotting the metrics
_ = (
pd.DataFrame(history.history)
.reset_index()
.rename(columns={"index": "epoch"})
.assign(epoch=lambda df: df.epoch + 1)
)
df = pd.concat(
[
_.iloc[:, 0:3].assign(fold="training"),
_.iloc[:, [0, -2, -1]]
.rename(columns={"val_accuracy": "accuracy", "val_loss": "loss"})
.assign(fold="validation"),
],
axis=0,
).reset_index(drop=True)
base = alt.Chart(df).mark_line(point=True)
loss = base.encode(x="epoch:Q", y="loss", color="fold", tooltip=["epoch", alt.Tooltip("loss", format=",.2f")])
accuracy = base.encode(x="epoch:Q", y="accuracy", color="fold", tooltip=["epoch", alt.Tooltip("accuracy", format=",.2f")] )
loss | accuracy
This model takes 10 minutes to run and achieves 46% accuracy on the test data. Although this is not terrible for 100-class data (a random classifier gets 1% accuracy), searching the web we see results around 75%. Typically, it takes a lot of architecture carpentry, fiddling with regularization, and time to achieve such results.
loss_and_metrics = model.evaluate(X_test, Y_test, verbose=2)
print(f"Test Loss: {loss_and_metrics[0]:.3f}")
print(f"Test Accuracy: {loss_and_metrics[1]:.3f}")
313/313 - 2s - loss: 2.4390 - accuracy: 0.4524 - 2s/epoch - 6ms/step
Test Loss: 2.439
Test Accuracy: 0.452