Fix Theano tests
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@ -142,6 +142,7 @@ class Convolution1D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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length = conv_output_length(input_shape[1],
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@ -434,6 +435,7 @@ class Convolution2D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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if self.dim_ordering == 'th':
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@ -982,6 +984,7 @@ class SeparableConvolution2D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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if self.dim_ordering == 'th':
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@ -1179,6 +1182,7 @@ class Convolution3D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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if self.dim_ordering == 'th':
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@ -371,6 +371,7 @@ class ConvLSTM2D(ConvRecurrent2D):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def reset_states(self):
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assert self.stateful, 'Layer must be stateful.'
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@ -723,6 +723,7 @@ class Dense(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def call(self, x, mask=None):
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output = K.dot(x, self.W)
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@ -891,6 +892,7 @@ class MaxoutDense(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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assert input_shape and len(input_shape) == 2
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@ -1028,6 +1030,7 @@ class Highway(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def call(self, x, mask=None):
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y = K.dot(x, self.W_carry)
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@ -1168,6 +1171,7 @@ class TimeDistributedDense(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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return (input_shape[0], input_shape[1], self.output_dim)
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@ -110,6 +110,7 @@ class Embedding(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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self.built = True
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def compute_mask(self, x, mask=None):
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if not self.mask_zero:
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@ -139,6 +139,7 @@ class LocallyConnected1D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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length = conv_output_length(input_shape[1],
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@ -333,6 +334,7 @@ class LocallyConnected2D(Layer):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def get_output_shape_for(self, input_shape):
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if self.dim_ordering == 'th':
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@ -325,6 +325,7 @@ class SimpleRNN(Recurrent):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def reset_states(self):
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assert self.stateful, 'Layer must be stateful.'
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@ -515,6 +516,7 @@ class GRU(Recurrent):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def reset_states(self):
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assert self.stateful, 'Layer must be stateful.'
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@ -745,6 +747,7 @@ class LSTM(Recurrent):
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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self.built = True
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def reset_states(self):
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assert self.stateful, 'Layer must be stateful.'
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@ -389,7 +389,8 @@ def test_zero_padding_1d():
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nb_samples = 2
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input_dim = 2
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nb_steps = 5
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input = np.ones((nb_samples, nb_steps, input_dim))
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shape = (nb_samples, nb_steps, input_dim)
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input = np.ones(shape)
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# basic test
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layer_test(convolutional.ZeroPadding1D,
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@ -404,6 +405,7 @@ def test_zero_padding_1d():
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# correctness test
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layer = convolutional.ZeroPadding1D(padding=2)
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layer.build(shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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for offset in [0, 1, -1, -2]:
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@ -411,6 +413,7 @@ def test_zero_padding_1d():
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assert_allclose(np_output[:, 2:-2, :], 1.)
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layer = convolutional.ZeroPadding1D(padding=(1, 2))
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layer.build(shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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for left_offset in [0]:
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@ -449,6 +452,7 @@ def test_zero_padding_2d():
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# correctness test
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layer = convolutional.ZeroPadding2D(padding=(2, 2))
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output = layer(K.variable(input))
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layer.build(input.shape)
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np_output = K.eval(output)
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if dim_ordering == 'tf':
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for offset in [0, 1, -1, -2]:
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@ -462,6 +466,7 @@ def test_zero_padding_2d():
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assert_allclose(np_output[:, 2:-2, 2:-2, :], 1.)
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layer = convolutional.ZeroPadding2D(padding=(1, 2, 3, 4))
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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if dim_ordering == 'tf':
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@ -505,6 +510,7 @@ def test_zero_padding_3d():
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# correctness test
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layer = convolutional.ZeroPadding3D(padding=(2, 2, 2))
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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for offset in [0, 1, -1, -2]:
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@ -542,6 +548,7 @@ def test_upsampling_2d():
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layer = convolutional.UpSampling2D(
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size=(length_row, length_col),
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dim_ordering=dim_ordering)
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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if dim_ordering == 'th':
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@ -582,6 +589,7 @@ def test_upsampling_3d():
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layer = convolutional.UpSampling3D(
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size=(length_dim1, length_dim2, length_dim3),
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dim_ordering=dim_ordering)
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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if dim_ordering == 'th':
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@ -641,6 +649,7 @@ def test_cropping_2d():
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# correctness test
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layer = convolutional.Cropping2D(cropping=cropping,
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dim_ordering=dim_ordering)
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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# compare with numpy
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@ -681,6 +690,7 @@ def test_cropping_3d():
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# correctness test
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layer = convolutional.Cropping3D(cropping=cropping,
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dim_ordering=dim_ordering)
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layer.build(input.shape)
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output = layer(K.variable(input))
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np_output = K.eval(output)
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# compare with numpy
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@ -110,6 +110,7 @@ def test_recurrent_convolutional():
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'border_mode': "same"}
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layer = convolutional_recurrent.ConvLSTM2D(**kwargs)
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layer.build(input.shape)
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output = layer(K.variable(np.ones(input.shape)))
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K.eval(output)
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@ -129,6 +129,7 @@ def test_regularizer(layer_class):
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U_regularizer=regularizers.WeightRegularizer(l1=0.01),
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b_regularizer='l2')
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shape = (nb_samples, timesteps, embedding_dim)
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layer.build(shape)
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output = layer(K.variable(np.ones(shape)))
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K.eval(output)
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