from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
/Users/jannes/anaconda/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend. /Users/jannes/anaconda/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5 return f(*args, **kwds)
Couldn't import dot_parser, loading of dot files will not be possible.
from keras.models import Sequential
from keras.layers import Dense
x_train.shape = (60000, 28 * 28)
x_test.shape = (10000, 28 * 28)
x_train = x_train / 255
x_test = x_test / 255
model = Sequential()
model.add(Dense(512,activation='relu',input_dim= 28*28))
model.add(Dense(512,activation='relu'))
model.add(Dense(512,activation='relu'))
model.add(Dense(10,activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['acc'])
from keras.callbacks import TensorBoard
import tensorflow as tf
from tensorflow.python import debug as tf_debug
import keras
keras.backend.set_session(
tf_debug.TensorBoardDebugWrapperSession(tf.Session(), "localhost:2018"))
tb = TensorBoard(log_dir='./logs/test2',
histogram_freq=1,
batch_size=32,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
hist = model.fit(x_train*255,y_train,
batch_size=128,
epochs=5,callbacks=[tb],
validation_data=(x_test*255,y_test))
Train on 60000 samples, validate on 10000 samples Epoch 1/5 60000/60000 [==============================] - 9s 149us/step - loss: 13.4057 - acc: 0.1680 - val_loss: 14.2823 - val_acc: 0.1139 Epoch 2/5 60000/60000 [==============================] - 9s 144us/step - loss: 12.8723 - acc: 0.2014 - val_loss: 12.7043 - val_acc: 0.2118 Epoch 3/5 60000/60000 [==============================] - 8s 136us/step - loss: 12.7616 - acc: 0.2082 - val_loss: 12.7011 - val_acc: 0.2120 Epoch 4/5 60000/60000 [==============================] - 9s 150us/step - loss: 12.7532 - acc: 0.2088 - val_loss: 12.7011 - val_acc: 0.2120 Epoch 5/5 60000/60000 [==============================] - 9s 143us/step - loss: 13.8286 - acc: 0.1420 - val_loss: 14.2887 - val_acc: 0.1135