data_in_shape = (5, 5, 5, 2)
conv = Conv3D(4, (3,3,3), strides=(1,1,1), padding='valid',
              data_format='channels_last', dilation_rate=(1,1,1),
              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(130)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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: Conv3D
[convolutional.Conv3D.0] 4 3x3x3 filters on 5x5x5x2 input, strides=(1,1,1), padding='valid', data_format='channels_last', dilation_rate=(1,1,1), activation='linear', use_bias=True
End of explanation
"""


data_in_shape = (4, 4, 4, 2)
conv = Conv3D(2, (3,3,3), strides=(1,1,1), padding='valid',
              data_format='channels_last', dilation_rate=(1,1,1),
              activation='sigmoid', 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(131)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
#print('b shape:', weights[1].shape)
#print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.1] 2 3x3x3 filters on 4x4x4x2 input, strides=(1,1,1), padding='valid', data_format='channels_last', dilation_rate=(1,1,1), activation='sigmoid', use_bias=False
End of explanation
"""


data_in_shape = (4, 4, 3, 2)
conv = Conv3D(2, (3,3,3), strides=(1,1,1), padding='same',
              data_format='channels_last', dilation_rate=(1,1,1),
              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(132)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.2] 2 3x3x3 filters on 4x4x3x2 input, strides=(1,1,1), padding='same', data_format='channels_last', dilation_rate=(1,1,1), activation='relu', use_bias=True
End of explanation
"""


data_in_shape = (4, 4, 3, 2)
conv = Conv3D(2, (3,3,2), strides=(2,1,1), padding='same',
              data_format='channels_last', dilation_rate=(1,1,1),
              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(133)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.3] 2 3x3x2 filters on 4x4x3x2 input, strides=(2,1,1), padding='same', data_format='channels_last', dilation_rate=(1,1,1), activation='relu', use_bias=True
End of explanation
"""


data_in_shape = (6, 6, 4, 2)
conv = Conv3D(2, (3,3,3), strides=(3,3,2), padding='same',
              data_format='channels_last', dilation_rate=(1,1,1),
              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(134)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.4] 2 3x3x3 filters on 6x6x4x2 input, strides=(3,3,2), padding='same', data_format='channels_last', dilation_rate=(1,1,1), activation='relu', use_bias=True
End of explanation
"""


data_in_shape = (6, 4, 4, 2)
conv = Conv3D(2, (3,3,3), strides=(1,1,1), padding='valid',
              data_format='channels_last', dilation_rate=(2,1,1),
              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(135)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.5] 2 3x3x3 filters on 6x4x4x2 input, strides=(1,1,1), padding='valid', data_format='channels_last', dilation_rate=(2,1,1), activation='relu', use_bias=True
End of explanation
"""


data_in_shape = (4, 4, 3, 2)
conv = Conv3D(2, (3,3,3), strides=(1,1,1), padding='same',
              data_format='channels_last', dilation_rate=(1,2,1),
              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(136)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].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.Conv3D.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.Conv3D.6] 2 3x3x3 filters on 4x4x3x2 input, strides=(1,1,1), padding='same', data_format='channels_last', dilation_rate=(1,2,1), activation='relu', use_bias=True
End of explanation
"""


import os

filename = '../../../test/data/layers/convolutional/Conv3D.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
"""


