def test_conv2d(self): # TF kernel shape: (rows, cols, input_depth, depth) # channels_first input shape: (n, input_depth, rows, cols) for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]: for kernel_shape in [(2, 2, 3, 4), (4, 3, 3, 4)]: for padding in ['valid', 'same']: xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val)) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval( KTH.conv2d(xth, kernel_th, data_format='channels_first')) ztf = KTF.eval( KTF.conv2d(xtf, kernel_tf, data_format='channels_first')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05) input_shape = (1, 6, 5, 3) kernel_shape = (3, 3, 3, 2) xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val)) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, data_format='channels_last')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, data_format='channels_last')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05)
def test_conv2d(self): # TF kernel shape: (rows, cols, input_depth, depth) # channels_first input shape: (n, input_depth, rows, cols) for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]: for kernel_shape in [(2, 2, 3, 4), (4, 3, 3, 4)]: for padding in ['valid', 'same']: xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val)) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, data_format='channels_first')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, data_format='channels_first')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05) input_shape = (1, 6, 5, 3) kernel_shape = (3, 3, 3, 2) xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val)) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, data_format='channels_last')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, data_format='channels_last')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05)
def test_conv2d(self): # TH kernel shape: (depth, input_depth, rows, cols) # TF kernel shape: (rows, cols, input_depth, depth) for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]: for kernel_shape in [(4, 3, 2, 2), (4, 3, 3, 4)]: xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable( convert_kernel(kernel_val, dim_ordering='th')) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, dim_ordering='th')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, dim_ordering='th')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05) input_shape = (1, 6, 5, 3) kernel_shape = (3, 3, 3, 2) xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val, dim_ordering='tf')) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, dim_ordering='tf')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, dim_ordering='tf')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05)
def call(self, x, training=None): s = K.conv2d(x, kernel=self.kernel, strides=self.strides, padding='valid') # ========================================= if self.use_bias: s = K.bias_add( s, self.bias, data_format=self.data_format) if self.scale is not None: s = tf.multiply(s, self.scale) # =========================================== return s
def test_conv2d(self): # TH kernel shape: (depth, input_depth, rows, cols) # TF kernel shape: (rows, cols, input_depth, depth) for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]: for kernel_shape in [(4, 3, 2, 2), (4, 3, 3, 4)]: xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val)) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th)) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf)) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05) input_shape = (1, 6, 5, 3) kernel_shape = (3, 3, 3, 2) xval = np.random.random(input_shape) xth = KTH.variable(xval) xtf = KTF.variable(xval) kernel_val = np.random.random(kernel_shape) - 0.5 kernel_th = KTH.variable(convert_kernel(kernel_val, dim_ordering='tf')) kernel_tf = KTF.variable(kernel_val) zth = KTH.eval(KTH.conv2d(xth, kernel_th, dim_ordering='tf')) ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, dim_ordering='tf')) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05)