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Keras_tensorflow.py
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Keras_tensorflow.py
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# coding: utf-8
# In[1]:
import tensorflow as tf
mnist=tf.keras.datasets.mnist #28X28 handwritten 0-9
(x_train,y_train),(x_test,y_test)=mnist.load_data()
#import matplotlib.pyplot as plt
#plt.imshow(x_train[0], cmap=plt.cm.binary)
#plt.show
#print(x_train[0])
#normalize to scale
x_train=tf.keras.utils.normalize(x_train)
x_test=tf.keras.utils.normalize(x_test)
model=tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
# In[13]:
val_loss, val_acc=model.evaluate(x_test, y_test)
print(val_loss, val_acc)
# In[10]:
import matplotlib.pyplot as plt
#plt.imshow(x_train[0], cmap=plt.cm.binary)
#plt.show()
#print(x_train[0])
#model.save('epic_num_reader.model')
#new_model=tf.keras.models.load_model('epic_num_reader.model')
pred=model.predict([x_test])
print(pred)
# In[25]:
import numpy as np
print(np.argmax(pred[7]))
# In[26]:
plt.imshow(x_test[7])
plt.show()
# In[2]:
type(pred)
# In[ ]: