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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 SeparableConv2D
[convolutional.SeparableConv2D.0] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=1, activation='linear', use_bias=True
Step1:  [convolutional.SeparableConv2D.1] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True
Step2:  [convolutional.SeparableConv2D.2] 16 3x3 filters on 5x5x4 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=3, activation='relu', use_bias=True
Step3:  [convolutional.SeparableConv2D.3] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='valid', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True
Step4:  [convolutional.SeparableConv2D.4] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True
Step5:  [convolutional.SeparableConv2D.5] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=False
Step6:  [convolutional.SeparableConv2D.6] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True
Step7:  export for Keras.js tests
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Python Code:
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(1,1),
                       padding='valid', data_format='channels_last',
                       depth_multiplier=1, activation='linear', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(160)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.0'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: SeparableConv2D
[convolutional.SeparableConv2D.0] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=1, activation='linear', use_bias=True
End of explanation
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(1,1),
                       padding='valid', data_format='channels_last',
                       depth_multiplier=2, activation='relu', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(161)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.1'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.1] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True
End of explanation
data_in_shape = (5, 5, 4)
conv = SeparableConv2D(16, (3,3), strides=(1,1),
                       padding='valid', data_format='channels_last',
                       depth_multiplier=3, activation='relu', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(162)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.2'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.2] 16 3x3 filters on 5x5x4 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=3, activation='relu', use_bias=True
End of explanation
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(2,2),
                       padding='valid', data_format='channels_last',
                       depth_multiplier=1, activation='relu', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(163)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.3'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.3] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='valid', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True
End of explanation
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(1,1),
                       padding='same', data_format='channels_last',
                       depth_multiplier=1, activation='relu', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(164)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.4'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.4] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True
End of explanation
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(1,1),
                       padding='same', data_format='channels_last',
                       depth_multiplier=2, activation='relu', use_bias=False)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(165)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
# print('b shape:', weights[2].shape)
# print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.5'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.5] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=False
End of explanation
data_in_shape = (5, 5, 2)
conv = SeparableConv2D(4, (3,3), strides=(2,2),
                       padding='same', data_format='channels_last',
                       depth_multiplier=2, activation='relu', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(166)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('depthwise_kernel shape:', weights[0].shape)
print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist()))
print('pointwise_kernel shape:', weights[1].shape)
print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist()))
print('b shape:', weights[2].shape)
print('b:', format_decimal(weights[2].ravel().tolist()))
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)
DATA['convolutional.SeparableConv2D.6'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}
Explanation: [convolutional.SeparableConv2D.6] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True
End of explanation
import os
filename = '../../../test/data/layers/convolutional/SeparableConv2D.json'
if not os.path.exists(os.path.dirname(filename)):
    os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as f:
    json.dump(DATA, f)
print(json.dumps(DATA))
Explanation: export for Keras.js tests
End of explanation
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