Neural Network: Callback and Checkpoints¶

For classifying MNIST digits.

Load dependencies¶

In [1]:

#pip install tensorflow==2.0.0-beta0
#pip install --upgrade tensorflow==2.0.0-beta0

In [2]:

import tensorflow as tf
from tensorflow import keras  # tf.keras
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sklearn
import sys
import time

In [3]:

sns.set()
%matplotlib inline
%load_ext tensorboard

In [4]:

print("python", sys.version)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)

python 3.7.1 (default, Dec 14 2018, 13:28:58) 
[Clang 4.0.1 (tags/RELEASE_401/final)]
matplotlib 3.0.2
numpy 1.15.4
pandas 0.23.4
sklearn 0.20.1
tensorflow 2.0.0-beta0
tensorflow.python.keras.api._v2.keras 2.2.4-tf

Load data¶

In [5]:

fashion_mnist = keras.datasets.fashion_mnist
(X_train_full, y_train_full), (X_test, y_test) = (
    fashion_mnist.load_data())
X_valid, X_train = X_train_full[:5000], X_train_full[5000:]
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]

In [6]:

X_train.shape, y_train.shape

Out[6]:

((55000, 28, 28), (55000,))

In [7]:

class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
               "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]

In [8]:

#X_train[0]

In [9]:

n_rows = 5
n_cols = 10
plt.figure(figsize=(n_cols*1.4, n_rows * 1.6))
for row in range(n_rows):
    for col in range(n_cols):
        index = n_cols * row + col
        plt.subplot(n_rows, n_cols, index + 1)
        plt.imshow(X_train[index], cmap="binary", interpolation="nearest")
        plt.axis('off')
        plt.title(class_names[y_train[index]])
plt.show()

In [10]:

y_train[0:50]

Out[10]:

array([4, 0, 7, 9, 9, 9, 4, 4, 3, 4, 0, 1, 8, 6, 3, 6, 4, 3, 2, 8, 7, 3,
       4, 7, 1, 3, 4, 2, 0, 8, 5, 5, 9, 1, 5, 3, 5, 9, 0, 3, 9, 6, 4, 2,
       9, 0, 8, 3, 3, 2], dtype=uint8)

In [11]:

class_names[y_train[0]]

Out[11]:

'Coat'

Preprocess data : Feature Scaling¶

When using Gradient Descent, it is usually best to ensure that the features all have a similar scale, preferably with a Normal distribution. Try to standardize the pixel values and see if this improves the performance of your neural network.

Tips:

For each feature (pixel intensity), you must subtract the mean() of that feature (across all instances, so use axis=0) and divide by its standard deviation (std(), again axis=0). Alternatively, you can use Scikit-Learn's StandardScaler.
Make sure you compute the means and standard deviations on the training set, and use these statistics to scale the training set, the validation set and the test set (you should not fit the validation set or the test set, and computing the means and standard deviations counts as "fitting").

In [13]:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train.astype(np.float32)\
                                      .reshape(-1, 28 * 28)).reshape(-1, 28, 28)
X_valid_scaled = scaler.transform(X_valid.astype(np.float32)\
                                  .reshape(-1, 28 * 28)).reshape(-1, 28, 28)
X_test_scaled = scaler.transform(X_test.astype(np.float32)\
                                 .reshape(-1, 28 * 28)).reshape(-1, 28, 28)

Design neural network architecture¶

In [14]:

model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[28, 28]),
    keras.layers.Dense(300, activation="relu"),
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])

In [15]:

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 784)               0         
_________________________________________________________________
dense (Dense)                (None, 300)               235500    
_________________________________________________________________
dense_1 (Dense)              (None, 100)               30100     
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1010      
=================================================================
Total params: 266,610
Trainable params: 266,610
Non-trainable params: 0
_________________________________________________________________

Configure model: Callbacks and Checkpoints¶

The fit() method accepts a callbacks argument. Try training your model with a large number of epochs, a validation set, and with a few callbacks from keras.callbacks:

TensorBoard: specify a log directory. It should be a subdirectory of a root logdir, such as ./my_logs/run_1, and it should be different every time you train your model. You can use a timestamp in the subdirectory's path to ensure that it changes at every run.
EarlyStopping: specify patience=5
ModelCheckpoint: specify the path of the checkpoint file to save (e.g., "my_mnist_model.h5") and set save_best_only=True

Notice that the EarlyStopping callback will interrupt training before it reaches the requested number of epochs. This reduces the risk of overfitting.

In [23]:

model.compile(loss="sparse_categorical_crossentropy",
              optimizer=keras.optimizers.SGD(1e-3),\
              metrics=["accuracy"])

Train!¶

In [24]:

mkdir log

mkdir: log: File exists

In [25]:

logdir = os.path.join("./log/run_1", "run_{}".format(time.time()))

callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.EarlyStopping(patience=5),
    keras.callbacks.ModelCheckpoint("my_mnist_model.h5", save_best_only=True),
]

In [26]:

history = model.fit(X_train_scaled,\
                    y_train,\
                    batch_size=128,\
                    epochs=20,
                    validation_data=(X_valid_scaled, y_valid),
                   callbacks=callbacks)

WARNING: Logging before flag parsing goes to stderr.
W1228 16:58:45.364671 4328199616 deprecation.py:323] From /Users/admin/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

Train on 55000 samples, validate on 5000 samples
Epoch 1/20
55000/55000 [==============================] - 2s 40us/sample - loss: 1.4332 - accuracy: 0.5387 - val_loss: 0.9500 - val_accuracy: 0.7058
Epoch 2/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.8561 - accuracy: 0.7255 - val_loss: 0.7478 - val_accuracy: 0.7598
Epoch 3/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.7228 - accuracy: 0.7617 - val_loss: 0.6602 - val_accuracy: 0.7816
Epoch 4/20
55000/55000 [==============================] - 2s 32us/sample - loss: 0.6535 - accuracy: 0.7809 - val_loss: 0.6091 - val_accuracy: 0.7990
Epoch 5/20
55000/55000 [==============================] - 2s 35us/sample - loss: 0.6085 - accuracy: 0.7931 - val_loss: 0.5736 - val_accuracy: 0.8116
Epoch 6/20
55000/55000 [==============================] - 2s 31us/sample - loss: 0.5760 - accuracy: 0.8018 - val_loss: 0.5479 - val_accuracy: 0.8172
Epoch 7/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.5510 - accuracy: 0.8096 - val_loss: 0.5275 - val_accuracy: 0.8250
Epoch 8/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.5311 - accuracy: 0.8150 - val_loss: 0.5122 - val_accuracy: 0.8302
Epoch 9/20
55000/55000 [==============================] - 2s 30us/sample - loss: 0.5147 - accuracy: 0.8208 - val_loss: 0.4988 - val_accuracy: 0.8346
Epoch 10/20
55000/55000 [==============================] - 2s 34us/sample - loss: 0.5010 - accuracy: 0.8256 - val_loss: 0.4877 - val_accuracy: 0.8364
Epoch 11/20
55000/55000 [==============================] - 2s 31us/sample - loss: 0.4893 - accuracy: 0.8291 - val_loss: 0.4782 - val_accuracy: 0.8390
Epoch 12/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.4790 - accuracy: 0.8322 - val_loss: 0.4704 - val_accuracy: 0.8404
Epoch 13/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.4699 - accuracy: 0.8352 - val_loss: 0.4628 - val_accuracy: 0.8444
Epoch 14/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.4618 - accuracy: 0.8380 - val_loss: 0.4565 - val_accuracy: 0.8450
Epoch 15/20
55000/55000 [==============================] - 2s 29us/sample - loss: 0.4545 - accuracy: 0.8401 - val_loss: 0.4505 - val_accuracy: 0.8482
Epoch 16/20
55000/55000 [==============================] - 2s 28us/sample - loss: 0.4480 - accuracy: 0.8426 - val_loss: 0.4453 - val_accuracy: 0.8498
Epoch 17/20
55000/55000 [==============================] - 2s 29us/sample - loss: 0.4419 - accuracy: 0.8446 - val_loss: 0.4406 - val_accuracy: 0.8508
Epoch 18/20
55000/55000 [==============================] - 2s 30us/sample - loss: 0.4363 - accuracy: 0.8468 - val_loss: 0.4362 - val_accuracy: 0.8520
Epoch 19/20
55000/55000 [==============================] - 2s 34us/sample - loss: 0.4312 - accuracy: 0.8484 - val_loss: 0.4318 - val_accuracy: 0.8524
Epoch 20/20
55000/55000 [==============================] - 2s 34us/sample - loss: 0.4264 - accuracy: 0.8499 - val_loss: 0.4286 - val_accuracy: 0.8538

In [116]:

model.evaluate(X_test_scaled, y_test)

10000/10000 [==============================] - 0s 39us/sample - loss: 0.4680 - accuracy: 0.8349

Out[116]:

[0.4679875907897949, 0.8349]

In [27]:

def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    plt.gca().set_ylim(0, 1)
    plt.show()

In [28]:

plot_learning_curves(history)

The early stopping callback only stopped training after 10 epochs without progress, so your model may already have started to overfit the training set. Fortunately, since the ModelCheckpoint callback only saved the best models (on the validation set), the last saved model is the best on the validation set, so try loading it using keras.models.load_model(). Finally evaluate it on the test set.

In [29]:

model = keras.models.load_model("my_mnist_model.h5")

In [30]:

model.evaluate(X_valid_scaled, y_valid)

5000/5000 [==============================] - 0s 41us/sample - loss: 0.4286 - accuracy: 0.8538

Out[30]:

[0.428587375164032, 0.8538]

Performing Inference¶

In [31]:

model.evaluate(X_test, y_test)

10000/10000 [==============================] - 0s 38us/sample - loss: 6.0076 - accuracy: 0.6082

Out[31]:

[6.007626473999023, 0.6082]

In [32]:

X_valid.shape

Out[32]:

(5000, 28, 28)

In [33]:

n_new = 10
X_new = X_test[:n_new]
y_proba = model.predict(X_new)
y_proba.round(2)

Out[33]:

array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 1.  , 0.  ],
       [0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 0.  , 0.99, 0.  , 0.01, 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 0.  , 0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 0.  , 0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 1.  , 0.  ],
       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 1.  , 0.  ]],
      dtype=float32)

In [34]:

y_pred = model.predict_classes(X_new)
y_pred

Out[34]:

array([8, 2, 1, 1, 2, 1, 4, 4, 8, 8])

In [35]:

for item in y_pred:
    print(class_names[item])

Bag
Pullover
Trouser
Trouser
Pullover
Trouser
Coat
Coat
Bag
Bag

In [38]:

plt.imshow(X_valid[0].reshape(28,28))

Out[38]:

<matplotlib.image.AxesImage at 0x1a2d4de978>

In [ ]: