update

Author: bob7783

Diff for: cnn_class/keras_example.py

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+# https://deeplearningcourses.com/c/advanced-computer-vision
+# https://www.udemy.com/advanced-computer-vision
+
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+from keras.models import Sequential, Model
+from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+from datetime import datetime
+from scipy.io import loadmat
+from sklearn.utils import shuffle
+
+from benchmark import get_data, error_rate
+
+
+# helper
+# def y2indicator(Y):
+#   N = len(Y)
+#   K = len(set(Y))
+#   I = np.zeros((N, K))
+#   I[np.arange(N), Y] = 1
+#   return I
+
+def rearrange(X):
+    # input is (32, 32, 3, N)
+    # output is (N, 32, 32, 3)
+    # N = X.shape[-1]
+    # out = np.zeros((N, 32, 32, 3), dtype=np.float32)
+    # for i in xrange(N):
+    #     for j in xrange(3):
+    #         out[i, :, :, j] = X[:, :, j, i]
+    # return out / 255
+    return (X.transpose(3, 0, 1, 2) / 255.).astype(np.float32)
+
+
+# get the data
+train, test = get_data()
+
+# Need to scale! don't leave as 0..255
+# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)
+# So flatten it and make it 0..9
+# Also need indicator matrix for cost calculation
+Xtrain = rearrange(train['X'])
+Ytrain = train['y'].flatten() - 1
+del train
+
+Xtest  = rearrange(test['X'])
+Ytest  = test['y'].flatten() - 1
+del test
+
+
+
+# get shapes
+K = len(set(Ytrain))
+
+
+
+# make the CNN
+i = Input(shape=Xtrain.shape[1:])
+x = Conv2D(filters=20, kernel_size=(5, 5))(i)
+x = BatchNormalization()(x)
+x = Activation('relu')(x)
+x = MaxPooling2D()(x)
+
+x = Conv2D(filters=50, kernel_size=(5, 5))(x)
+x = BatchNormalization()(x)
+x = Activation('relu')(x)
+x = MaxPooling2D()(x)
+
+x = Flatten()(x)
+x = Dense(units=500)(x)
+x = Activation('relu')(x)
+x = Dropout(0.3)(x)
+x = Dense(units=K)(x)
+x = Activation('softmax')(x)
+
+model = Model(inputs=i, outputs=x)
+
+
+# list of losses: https://keras.io/losses/
+# list of optimizers: https://keras.io/optimizers/
+# list of metrics: https://keras.io/metrics/
+model.compile(
+  loss='sparse_categorical_crossentropy',
+  optimizer='adam',
+  metrics=['accuracy']
+)
+
+# note: multiple ways to choose a backend
+# either theano, tensorflow, or cntk
+# https://keras.io/backend/
+
+
+# gives us back a <keras.callbacks.History object at 0x112e61a90>
+r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=10, batch_size=32)
+print("Returned:", r)
+
+# print the available keys
+# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])
+print(r.history.keys())
+
+# plot some data
+plt.plot(r.history['loss'], label='loss')
+plt.plot(r.history['val_loss'], label='val_loss')
+plt.legend()
+plt.show()
+
+# accuracies
+plt.plot(r.history['acc'], label='acc')
+plt.plot(r.history['val_acc'], label='val_acc')
+plt.legend()
+plt.show()
+
+