In [1]:
# Import libraries
In [2]:
# Load data
In [3]:
# Format data into 1D (single variable) long 2D arrays
In [4]:
# Conv net Model spec
def max_pool_2x1(tensor_in):
return tf.nn.max_pool(tensor_in, ksize=[1, 2, 1, 1], strides=[1, 2, 1, 1],
padding='SAME')
def conv_model(X, y):
# reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height
# final dimension being the number of color channels
X = tf.reshape(X, [-1, 100, 1, 1])
# first conv layer will compute 32 features for each 5x1 strip
with tf.variable_scope('conv_layer1'):
h_conv1 = skflow.ops.conv2d(X, n_filters=32, filter_shape=[5, 1],
bias=True, activation=tf.nn.relu)
h_pool1 = max_pool_2x1(h_conv1)
# second conv layer will compute 64 features for each 5x1 strip
with tf.variable_scope('conv_layer2'):
h_conv2 = skflow.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 1],
bias=True, activation=tf.nn.relu)
h_pool2 = max_pool_2x1(h_conv2)
# reshape tensor into a batch of vectors
h_pool2_flat = tf.reshape(h_pool2, [-1, 5 * 5 * 64])
# densely connected layer with 1024 neurons
h_fc1 = skflow.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)
return skflow.models.logistic_regression(h_fc1, y)
# # Training and predicting
# classifier3 = skflow.TensorFlowEstimator(
# model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,
# learning_rate=0.001)
In [ ]:
# Model fit - once and compare to linear regression
# Convnet
convnet = skflow.TensorFlowEstimator(
model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,
learning_rate=0.001,verbose=0)
convnet.fit(traindata,trainlabs1D-1)
# lr.fit(both_c,tt_c.squeeze())
# print(accuracy_score(testlabs1D-1,convnet.predict(testdata)))
In [5]:
scores_convnet = cross_validation.cross_val_score(convnet, both_c, tt_c.squeeze()-1, cv=5,scoring='accuracy') #'f1_weighted')
print(scores_convnet)
print(np.mean(scores_convnet))
In [6]:
# Weight inspection
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