updated to third week

SecondSunday/mnist_phone_number.py

@@ -1,16 +1,18 @@
 %matplotlib inline
 from tensorflow.examples.tutorials.mnist import input_data
 import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
 import matplotlib.pyplot as plt
 
 mnist = input_data.read_data_sets('./SecondSunday/mnist_data', one_hot=True)
 label_number = mnist.train.labels.argmax(axis=1)
 number_imgs = {str(i):mnist.train.images[np.argwhere(label_number == i).squeeze()] for i in range(10)}
-DATA_COUNT = 100
-phone_number_digits = np.random.randint(10**9,10**10,size=(DATA_COUNT,10))
+DATA_COUNT = 10240
+# phone_number_digits = np.random.randint(10**9,10**10,size=(DATA_COUNT,10))
 phone_number_digits = np.random.randint(10,size=(DATA_COUNT,10))
 phone_number_digits.astype(str)
-phone_number_strings = phone_number_digits.astype(str)
+phone_number_strings = pd.DataFrame(phone_number_digits.astype(str).T).apply(lambda x: ''.join(x)).values
 
 def pick_img(num):
     rand_idx = np.random.randint(number_imgs[num].shape[0])
@@ -27,20 +29,29 @@ def create_phone_images(phone_array):
     return np.array(phone_number_images).reshape(-1,28*280)
 
 phone_number_imgs = create_phone_images(phone_number_strings)
+train_imgs,test_imgs,train_digits,test_digits = train_test_split(phone_number_imgs,phone_number_digits)
 
 from keras.models import Sequential
 from keras.layers import Dense, Activation
 
-model = Sequential([
-    Dense(32, input_shape=(7840,)),
-    Activation('relu'),
-    Dense(10),
-    Activation('linear'),
-])
+# model = Sequential([
+#     Dense(32, input_shape=(7840,)),
+#     Activation('relu'),
+#     # Dense(24, input_shape=(32,)),
+#     # Activation('relu'),
+#     Dense(10),
+#     Activation('linear'),
+# ])
 
-model.compile(optimizer='rmsprop',
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
+# model.compile(optimizer='sgd',
+#               loss='mean_squared_error',
+#               metrics=['accuracy'])
+#
+# model.fit(train_imgs, train_digits,
+#           batch_size=128,
+#           epochs=100,
+#           validation_data=(test_imgs, test_digits))
 
-model.fit()
-# plt.imshow(phone_number_imgs[np.random.randint(phone_number_imgs.shape[0])])
+# img_idx = np.random.randint(phone_number_imgs.shape[0])
+# print(phone_number_strings[img_idx])
+# plt.imshow(phone_number_imgs[img_idx].reshape(28,280))