<jupyter_start><jupyter_text>pipeline 8<jupyter_code>data_in_shape = (8, 8, 2)

conv_0 = Conv2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_1 = Conv2D(4, 3, 3, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
pool_0 = MaxPooling2D(pool_size=(2, 2), strides=(1, 1), border_mode='same', dim_ordering='tf')

input_layer = Input(shape=data_in_shape)
x = conv_0(input_layer)
x = conv_1(x)
output_layer = pool_0(x)
model = Model(input=input_layer, output=output_layer)

np.random.seed(9000)
data_in = 2 * np.random.random(data_in_shape) - 1

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(9000 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)

result = model.predict(np.array([data_in]))

print({
    'input': {'data': format_decimal(data_in.ravel().tolist()), 'shape': list(data_in_shape)},
    'weights': [{'data': format_decimal(weights[i].ravel().tolist()), 'shape': list(weights[i].shape)} for i in range(len(weights))],
    'expected': {'data': format_decimal(result[0].ravel().tolist()), 'shape': list(result[0].shape)}
})<jupyter_output>{'input': {'shape': [8, 8, 2], 'data': [-0.41555038, 0.97461397, 0.75821321, -0.21914657, -0.97631015, -0.73121474, 0.33607099, -0.19573518, -0.90314981, 0.57328105, -0.31008384, -0.04097988, -0.7944409, -0.59867349, 0.11736359, 0.44927613, -0.45658166, 0.53144057, 0.88095177, -0.26818166, -0.78497489, 0.52071551, -0.77909559, 0.27316327, -0.70334372, 0.68283568, -0.42097229, -0.56161334, 0.32549452, -0.28786984, 0.91681983, -0.40536397, -0.20665999, -0.19036561, -0.42646282, 0.79665162, 0.15267526, 0.59278252, 0.73005423, -0.8377447, -0.40203092, 0.49379166, -0.17837407, -0.24601101, -0.4719636, 0.72086455, 0.6045019, -0.04170968, -0.07386621, -0.58061159, 0.7559498, 0.67788764, 0.15911773, -0.33300175, 0.27103546, 0.4685299, 0.7996605, 0.39635589, -0.62500296, 0.13468982, 0.41144838, 0.10678898, 0.13393713, -0.51624453, 0.75973462, -0.15889839, -0.13356371, -0.24345362, 0.54311613, 0.24951841, 0.86556601, 0.93970336, -0.44348215, -0.14442047, -0.7798827, -0.5337385, 0.2702182, -0.860[...]
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