fix merge conflict

.travis.yml

@@ -12,8 +12,9 @@ python:
   - "3.4"
 # command to install dependencies
 install:
-  - conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib pandas pytest h5py theano
+  - conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib pandas pytest h5py
   - pip install pytest-cov python-coveralls
+  - pip install git+git://github.com/Theano/Theano.git
 # command to run tests
 script:
   - PYTHONPATH=$PWD:$PYTHONPATH py.test -v --cov-report term-missing --cov keras tests/

docs/sources/callbacks.md

@@ -33,7 +33,10 @@ The `logs` dictionary will contain keys for quantities relevant to the current b
 keras.callbacks.ModelCheckpoint(filepath, verbose=0, save_best_only=False)
 ```
 
-Save the model after every epoch. If `save_best_only=True`, the latest best model according to the validation loss will not be overwritten. 
+Save the model after every epoch. If `save_best_only=True`, the latest best model according to the validation loss will not be overwritten.
+`filepath` can contain named formatting options, which will be filled the value of `epoch` and keys in `logs` (passed in `on_epoch_end`).
+
+For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then multiple files will be save with the epoch number and the validation loss.
 
 
 ```python

keras/activations.py

@@ -17,7 +17,7 @@ def softplus(x):
 
 
 def relu(x):
-    return (x + abs(x)) / 2.0
+    return T.nnet.relu(x)
 
 
 def tanh(x):

keras/callbacks.py

@@ -183,6 +183,7 @@ def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=False
         self.best = np.Inf
 
     def on_epoch_end(self, epoch, logs={}):
+        filepath = self.filepath.format(epoch=epoch, **logs)
         if self.save_best_only:
             current = logs.get(self.monitor)
             if current is None:
@@ -191,16 +192,16 @@ def on_epoch_end(self, epoch, logs={}):
                 if current < self.best:
                     if self.verbose > 0:
                         print("Epoch %05d: %s improved from %0.5f to %0.5f, saving model to %s"
-                              % (epoch, self.monitor, self.best, current, self.filepath))
+                              % (epoch, self.monitor, self.best, current, filepath))
                     self.best = current
-                    self.model.save_weights(self.filepath, overwrite=True)
+                    self.model.save_weights(filepath, overwrite=True)
                 else:
                     if self.verbose > 0:
                         print("Epoch %05d: %s did not improve" % (epoch, self.monitor))
         else:
             if self.verbose > 0:
-                print("Epoch %05d: saving model to %s" % (epoch, self.filepath))
-            self.model.save_weights(self.filepath, overwrite=True)
+                print("Epoch %05d: saving model to %s" % (epoch, filepath))
+            self.model.save_weights(filepath, overwrite=True)
 
 
 class EarlyStopping(Callback):

keras/layers/advanced_activations.py

@@ -12,7 +12,7 @@ def __init__(self, alpha=0.3):
 
     def get_output(self, train):
         X = self.get_input(train)
-        return ((X + abs(X)) / 2.0) + self.alpha * ((X - abs(X)) / 2.0)
+        return T.nnet.relu(X, self.alpha)
 
     def get_config(self):
         return {"name": self.__class__.__name__,
@@ -37,8 +37,8 @@ def __init__(self, input_shape, init='zero', weights=None):
 
     def get_output(self, train):
         X = self.get_input(train)
-        pos = ((X + abs(X)) / 2.0)
-        neg = self.alphas * ((X - abs(X)) / 2.0)
+        pos = T.nnet.relu(X)
+        neg = self.alphas * (X - abs(X)) * 0.5
         return pos + neg
 
     def get_config(self):
@@ -78,6 +78,7 @@ def get_config(self):
                 "alpha_init": self.alpha_init,
                 "beta_init": self.beta_init}
 
+
 class ThresholdedLinear(MaskedLayer):
     '''
         Thresholded Linear Activation
@@ -89,14 +90,15 @@ class ThresholdedLinear(MaskedLayer):
     def __init__(self, theta=1.0):
         super(ThresholdedLinear, self).__init__()
         self.theta = theta
-    
+
     def get_output(self, train):
         X = self.get_input(train)
-        return T.switch( abs(X) < self.theta, 0, X )
+        return T.switch(abs(X) < self.theta, 0, X)
 
     def get_config(self):
         return {"name": self.__class__.__name__,
-            "theta": self.theta}
+                "theta": self.theta}
+
 
 class ThresholdedReLu(MaskedLayer):
     '''
@@ -109,11 +111,11 @@ class ThresholdedReLu(MaskedLayer):
     def __init__(self, theta=1.0):
         super(ThresholdedReLu, self).__init__()
         self.theta = theta
-    
+
     def get_output(self, train):
         X = self.get_input(train)
-        return T.switch( X > self.theta, X, 0 )
+        return T.switch(X > self.theta, X, 0)
 
     def get_config(self):
         return {"name": self.__class__.__name__,
-            "theta": self.theta}
+                "theta": self.theta}

keras/layers/convolutional.py

@@ -105,14 +105,29 @@ def get_output(self, train=False):
         X = T.reshape(X, (X.shape[0], X.shape[1], X.shape[2], 1)).dimshuffle(0, 2, 1, 3)
 
         border_mode = self.border_mode
-        if border_mode == 'same':
-            border_mode = 'full'
-            assert self.subsample == (1, 1)
-
-        conv_out = T.nnet.conv.conv2d(X, self.W, border_mode=border_mode, subsample=self.subsample)
-        if self.border_mode == 'same':
-            shift_x = (self.filter_length - 1) // 2
-            conv_out = conv_out[:, :, shift_x:X.shape[2] + shift_x, :]
+        if on_gpu() and dnn.dnn_available():
+            if border_mode == 'same':
+                assert(self.subsample_length == 1)
+                pad_x = (self.filter_length - self.subsample_length) // 2
+                conv_out = dnn.dnn_conv(img=X,
+                                        kerns=self.W,
+                                        border_mode=(pad_x, 0))
+            else:
+                conv_out = dnn.dnn_conv(img=X,
+                                        kerns=self.W,
+                                        border_mode=border_mode,
+                                        subsample=self.subsample)
+        else:
+            if border_mode == 'same':
+                assert(self.subsample_length == 1)
+                border_mode = 'full'
+
+            conv_out = T.nnet.conv.conv2d(X, self.W,
+                                          border_mode=border_mode,
+                                          subsample=self.subsample)
+            if self.border_mode == 'same':
+                shift_x = (self.filter_length - 1) // 2
+                conv_out = conv_out[:, :, shift_x:X.shape[2] + shift_x, :]
 
         output = self.activation(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
         output = T.reshape(output, (output.shape[0], output.shape[1], output.shape[2])).dimshuffle(0, 2, 1)

keras/layers/core.py

@@ -380,10 +380,12 @@ class Reshape(Layer):
         Can't be used as first layer in a model (no fixed input!)
         First dimension is assumed to be nb_samples.
     '''
-    def __init__(self, *dims):
+    def __init__(self, *dims, **kwargs):
         super(Reshape, self).__init__()
-        if type(dims[0]) in [list, tuple]:
+        if len(dims) > 0 and type(dims[0]) in [list, tuple]:
             dims = dims[0]
+        if len(dims) == 0 and 'dims' in kwargs:
+            dims = kwargs['dims']
         self.dims = tuple(dims)
 
     @property

keras/models.py

@@ -312,17 +312,17 @@ def get_config(self, verbose=0):
             pp.pprint(config)
         return config
 
-    def to_yaml(self):
+    def to_yaml(self, **kwargs):
         # dump model configuration to yaml string
         import yaml
         config = self.get_config()
-        return yaml.dump(config)
+        return yaml.dump(config, **kwargs)
 
-    def to_json(self):
+    def to_json(self, **kwargs):
         # dump model configuration to json string
         import json
         config = self.get_config()
-        return json.dumps(config)
+        return json.dumps(config, **kwargs)
 
 
 class Sequential(Model, containers.Sequential):

tests/manual/check_callbacks.py

@@ -215,12 +215,22 @@ def on_train_end(self, logs={}):
 import warnings
 warnings.filterwarnings('error')
 try:
+    passed = False
     # this should issue a warning
     model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, callbacks =[checkpointer])
 except:
-    print("Tests passed")
-    import sys
-    sys.exit(0)
-
-raise Exception("Modelcheckpoint tests did not pass")
+    passed = True
+if not passed:
+    raise Exception("Modelcheckpoint tests did not pass")
 
+print("Test model checkpointer with pattern")
+filename = "model_weights.{epoch:04d}.hdf5"
+f = os.path.join(path, filename)
+nb_epoch = 3
+checkpointer = cbks.ModelCheckpoint(f)
+model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, callbacks=[checkpointer])
+for i in range(nb_epoch):
+    if not os.path.isfile(f.format(epoch=i)):
+        raise Exception("Model weights were not saved separately for each epoch")
+
+print("Tests passed")