GRU

examples/mnist_examples.py

@@ -56,7 +56,7 @@ def train(network_name, data):
 
     # We can test it on some examples from test
     test_set_x = data.test_set_x.get_value()
-    test_set_y = data.test_set_y.eval()
+    test_set_y = data.test_set_y.get_value()
 
     predicted_values = [np.argmax(prediction) for prediction in model.predict(test_set_x[:30])]
     true_values = [np.argmax(true_value) for true_value in test_set_y[:30]]

examples/model_template.py

@@ -71,7 +71,7 @@
 # make prediction
 # We can test it on some examples from test
 test_set_x = data.test_set_x.get_value()
-test_set_y = data.test_set_y.eval()
+test_set_y = data.test_set_y.get_value()
 
 predicted_values = [np.argmax(prediction) for prediction in model.predict(test_set_x[:30])]
 true_values = [np.argmax(true_value) for true_value in test_set_y[:30]]

examples/save_and_load_example.py

@@ -73,7 +73,7 @@
 # Make prediction
 # We can test it on some examples from test
 test_set_x = data.test_set_x.get_value()
-test_set_y = data.test_set_y.eval()
+test_set_y = data.test_set_y.get_value()
 
 predicted_values = [np.argmax(prediction) for prediction in model.predict(test_set_x[:30])]
 true_values = [np.argmax(true_value) for true_value in test_set_y[:30]]

tests/test_model.py

@@ -8,21 +8,11 @@
 class TestModel:
     @pytest.fixture(scope='module')
     def data(self):
-        from yadll.data import Data
-        from yadll.data import mnist_loader
-        # data = [[np.random.random((100, 25)).astype('float32'), np.random.random_integers(low=0, high=9, size=(100,)).astype('float32')],
-        #         [np.random.random((50, 25)).astype('float32'), np.random.random_integers(low=0, high=9, size=(50,)).astype('float32')],
-        #         [np.random.random((50, 25)).astype('float32'), np.random.random_integers(low=0, high=9, size=(50,)).astype('float32')]]
-        return Data(mnist_loader()) #, cast_y=True)
-
-    @pytest.fixture(scope='module')
-    def data_y_2D(self):
-        from yadll.data import Data
-        from yadll.data import mnist_loader
-        # data = [[np.random.random((100, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(100, ))).astype('float32')],
-        #         [np.random.random((50, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(50, ))).astype('float32')],
-        #         [np.random.random((50, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(50, ))).astype('float32')]]
-        return Data(mnist_loader())
+        from yadll.data import Data, one_hot_encoding
+        data = [[np.random.random((100, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(100, ))).astype('float32')],
+                [np.random.random((50, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(50, ))).astype('float32')],
+                [np.random.random((50, 25)).astype('float32'), one_hot_encoding(np.random.random_integers(low=0, high=9, size=(50, ))).astype('float32')]]
+        return Data(data)
 
     @pytest.fixture(scope='module')
     def hp(self):
@@ -49,15 +39,10 @@ def model_no_data(self, hp):
         from yadll.model import Model
         return Model(name='test_model', hyperparameters=hp)
 
-    @pytest.fixture(scope='module')
-    def model_y_2D(self, data_y_2D, hp):
-        from yadll.model import Model
-        return Model(name='test_model', data=data_y_2D, hyperparameters=hp)
-
     @pytest.fixture(scope='module')
     def input(self):
         from yadll.layers import InputLayer
-        return InputLayer(input_shape=(None, 784))
+        return InputLayer(input_shape=(None, 25))
 
     @pytest.fixture(scope='module')
     def layer(self, input):
@@ -133,22 +118,20 @@ def test_save_model(self, model, network, caplog):
         model_from_conf_file = Model()
         model_from_conf_file.from_conf(file='test_conf.yc')
 
-    def test_model(self, model, model_y_2D, network, network_unsupervised):
+    def test_model(self, model, network, network_unsupervised):
         model.network = network
         assert model.name == 'test_model'
         model.train()
         network_unsupervised.layers[0].input = None
         model.network = network_unsupervised
         model.pretrain()
         model.train()
-        model_y_2D.network = network
-        #model_y_2D.train()
-    #
-    # def test_predict(self, data, model, network):
-    #     model.network = network
-    #     model.train()
-    #     model.network.layers[0].input = None
-    #     model.predict(data.test_set_x.eval()[:10])
+
+    def test_predict(self, data, model, network):
+        model.network = network
+        model.train()
+        model.network.layers[0].input = None
+        model.predict(data.test_set_x.eval()[:10])
 
 
 

yadll/data.py

@@ -136,6 +136,7 @@ class Data(object):
 
     """
     def __init__(self, data, shared=True, borrow=True, cast_y=False):
+        self.data = data
         #TODO: Check data input
         if len(data) == 3:
             train_set, valid_set, test_set = data
@@ -167,4 +168,5 @@ def dataset(self):
                 (self.valid_set_x, self.valid_set_y),
                 (self.test_set_x, self.test_set_y)]
 
-
+    def shape(self):
+        return [(data[0].shape, data[1].shape) for data in self.data]

yadll/model.py

@@ -91,7 +91,8 @@ def __init__(self, network=None, data=None, hyperparameters=None, name='model',
         self.save_mode = None          # None, 'end' or 'each'
         self.index = T.iscalar()       # index to a [mini]batch
         self.epoch_index = T.ivector() # index per epoch
-        self.x = None # T.tensor3(name='x')    # the input data is presented as a matrix
+        self.x = T.matrix(name='x')    # the input data is presented as a matrix
+        self.y = T.matrix(name='y')
         self.report = dict()
 
     @timer(' Unsupervised Pre-Training')
@@ -140,15 +141,15 @@ def train(self, unsupervised_training=True, save_mode=None, shuffle=True):
         if self.data is None:
             raise NoDataFoundException
         # else:
-        #     y_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_y.ndim)
+        #     #y_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_y.ndim)
         #     if self.data.train_set_y.ndim == 1:
         #         self.y = T.ivector('y')  # the output labels are presented as 1D vector of[int] labels
         #     else:
         #         self.y = T.matrix('y')
-        y_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_y.ndim)
-        self.y = y_tensor_type('y')
-        x_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_x.ndim)
-        self.x = x_tensor_type('x')
+        # y_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_y.ndim)
+        # self.y = y_tensor_type('y')
+        # x_tensor_type = theano.tensor.TensorType(dtype=floatX, broadcastable=(False,)*self.data.train_set_x.ndim)
+        # self.x = x_tensor_type('x')
 
         if self.network is None:
             raise NoNetworkFoundException
@@ -211,7 +212,7 @@ def train(self, unsupervised_training=True, save_mode=None, shuffle=True):
         ################################################
         # Compiling functions for training, validating and testing the model
         logger.info('... Compiling the model')
-        train_model = theano.function(inputs=[self.index, self.epoch_index], outputs=cost, updates=updates, name='train', # mode='DebugMode',
+        train_model = theano.function(inputs=[self.index, self.epoch_index], outputs=cost, updates=updates, name='train', # on_unused_input='ignore', # mode='DebugMode',
                                       givens={self.x: self.data.train_set_x[self.epoch_index[self.index * self.hp.batch_size: (self.index + 1) * self.hp.batch_size]],
                                               self.y: self.data.train_set_y[self.epoch_index[self.index * self.hp.batch_size: (self.index + 1) * self.hp.batch_size]]})
 

yadll/network.py

@@ -1,7 +1,7 @@
 # -*- coding: UTF-8 -*-
 from collections import OrderedDict
 import cPickle
-
+import yadll
 from .layers import *
 
 import logging