def gen_batchnorm():
model = Sequential()
model.add(BatchNormalization(input_shape=(2, 1, 3)))
model.compile(optimizer='sgd', loss='mse')
params = [0] * 4
params[0] = np.array([3, 3, 3]) # gamma
params[1] = np.array([1, 2, -1]) # beta
params[2] = np.array([2, 2, 2]) # bias
params[3] = np.array([5, 5, 5]) # variance
data = np.ndarray((4, 2, 1, 3))
l = 0
for b in range(0, 4):
for h in range(0, 2):
for w in range(0, 1):
for c in range(0, 3):
l += 1
data[b, h, w, c] = l % 7 - 3
model.set_weights(params)
output = model.predict(data, batch_size=1)
wrt = js.JSONwriter(model, "tests/test_batchnorm_model.json")
wrt.save()
print(output.shape)
write("tests/test_batchnorm_output.json", output.tolist())
def gen_avgpool_1D_stride_2():
model = Sequential()
model.add(AveragePooling1D(pool_size=3, strides=2, input_shape=(5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 5, 2))
inp[0, 0, 0] = 0
inp[0, 0, 1] = 1
inp[0, 1, 0] = 2
inp[0, 1, 1] = 1
inp[0, 2, 0] = 0
inp[0, 2, 1] = 0
inp[0, 3, 0] = 2
inp[0, 3, 1] = 1
inp[0, 4, 0] = 2
inp[0, 4, 1] = 1
wrt = js.JSONwriter(model, "tests/test_avgpool_1D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_avgpool_1D_2_output.json", output.tolist())
def gen_maxpool_2D_stride_1_2():
model = Sequential()
model.add(
MaxPooling2D(pool_size=(2, 4), strides=(2, 1), input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 4, 5, 2))
inp[0, 0, 0, 0] = 0
inp[0, 0, 0, 1] = 1
inp[0, 0, 1, 0] = 2
inp[0, 0, 1, 1] = 1
inp[0, 0, 2, 0] = 0
inp[0, 0, 2, 1] = 0
inp[0, 0, 3, 0] = 2
inp[0, 0, 3, 1] = 1
inp[0, 0, 4, 0] = 2
inp[0, 0, 4, 1] = 1
inp[0, 1, 0, 0] = 0
inp[0, 1, 0, 1] = -1
inp[0, 1, 1, 0] = 1
inp[0, 1, 1, 1] = -2
inp[0, 1, 2, 0] = 3
inp[0, 1, 2, 1] = 1
inp[0, 1, 3, 0] = 2
inp[0, 1, 3, 1] = 0
inp[0, 1, 4, 0] = 2
inp[0, 1, 4, 1] = -3
inp[0, 2, 0, 0] = 1
inp[0, 2, 0, 1] = 2
inp[0, 2, 1, 0] = -2
inp[0, 2, 1, 1] = 0
inp[0, 2, 2, 0] = 3
inp[0, 2, 2, 1] = -3
inp[0, 2, 3, 0] = 2
inp[0, 2, 3, 1] = 1
inp[0, 2, 4, 0] = 2
inp[0, 2, 4, 1] = 0
inp[0, 3, 0, 0] = 1
inp[0, 3, 0, 1] = 2
inp[0, 3, 1, 0] = 0
inp[0, 3, 1, 1] = -2
inp[0, 3, 2, 0] = 3
inp[0, 3, 2, 1] = 1
inp[0, 3, 3, 0] = 2
inp[0, 3, 3, 1] = 3
inp[0, 3, 4, 0] = -3
inp[0, 3, 4, 1] = 1
wrt = js.JSONwriter(model, "tests/test_maxpool_2D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_maxpool_2D_2_output.json", output.tolist())
def gen_conv_1D_stride_2():
model = Sequential()
model.add(Conv1D(3, 2, strides=2, input_shape=(6, 4)))
model.compile(optimizer='rmsprop', loss='mse')
inp, weight = data_generator((1, 6, 4), (2, 4, 3))
bias = np.ndarray(3)
bias[0] = 0.5
bias[1] = 1.5
bias[2] = 2.5
w = [weight, bias]
model.set_weights(w)
wrt = js.JSONwriter(model, "tests/test_conv_1D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_conv_1D_2_input.json", inp)
write("tests/test_conv_1D_2_output.json", output)
def gen_tanh():
model = Sequential()
model.add(Flatten(input_shape=(8, 1, 1)))
model.add(Dense(4))
model.add(Activation('tanh'))
model.compile(optimizer='rmsprop', loss='mse')
inp, weight = data_generator((1, 8, 1, 1), (8, 4))
bias = np.ndarray(4)
bias[0] = 0.5
bias[1] = 1.5
bias[2] = 1.0
bias[3] = 3.0
w = [weight, bias]
model.set_weights(w)
wrt = js.JSONwriter(model, "tests/test_tanh_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_tanh_input.json", inp)
write("tests/test_tanh_output.json", output)
def gen_flatten():
model = Sequential()
model.add(Flatten(input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 4, 5, 2))
inp[0, 0, 0, 0] = 0
inp[0, 0, 0, 1] = 1
inp[0, 0, 1, 0] = 2
inp[0, 0, 1, 1] = 1
inp[0, 0, 2, 0] = 0
inp[0, 0, 2, 1] = 0
inp[0, 0, 3, 0] = 2
inp[0, 0, 3, 1] = 1
inp[0, 0, 4, 0] = 2
inp[0, 0, 4, 1] = 1
inp[0, 1, 0, 0] = 0
inp[0, 1, 0, 1] = -1
inp[0, 1, 1, 0] = 1
inp[0, 1, 1, 1] = -2
inp[0, 1, 2, 0] = 3
inp[0, 1, 2, 1] = 1
inp[0, 1, 3, 0] = 2
inp[0, 1, 3, 1] = 0
inp[0, 1, 4, 0] = 2
inp[0, 1, 4, 1] = -3
inp[0, 2, 0, 0] = 1
inp[0, 2, 0, 1] = 2
inp[0, 2, 1, 0] = -2
inp[0, 2, 1, 1] = 0
inp[0, 2, 2, 0] = 3
inp[0, 2, 2, 1] = -3
inp[0, 2, 3, 0] = 2
inp[0, 2, 3, 1] = 1
inp[0, 2, 4, 0] = 2
inp[0, 2, 4, 1] = 0
inp[0, 3, 0, 0] = 1
inp[0, 3, 0, 1] = 2
inp[0, 3, 1, 0] = 0
inp[0, 3, 1, 1] = -2
inp[0, 3, 2, 0] = 3
inp[0, 3, 2, 1] = 1
inp[0, 3, 3, 0] = 2
inp[0, 3, 3, 1] = 3
inp[0, 3, 4, 0] = -3
inp[0, 3, 4, 1] = 1
wrt = js.JSONwriter(model, "tests/test_flat_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_flat_output.json", output.tolist())
def gen_gru():
model = Sequential()
model.add(
GRU(2,
activation='tanh',
recurrent_activation='relu',
implementation=1,
stateful=False,
batch_input_shape=(5, 3, 3)))
model.compile(optimizer='sgd', loss='mse')
kernel = np.ones((3, 6))
rec_kernel = np.ones((2, 6))
bias = np.array([1, 2, -1, 0, 3, 4]) / 10
k = 0
for h in range(0, 3):
for w in range(0, 6):
k += 1
kernel[h, w] = (k % 5 - 2) / 10
k = 0
for h in range(0, 2):
for w in range(0, 6):
k += 1
rec_kernel[h, w] = (k % 5 - 2) / 10
parameters = [kernel, rec_kernel, bias]
model.set_weights(parameters)
data = np.ndarray((5, 3, 3))
l = 0
for b in range(0, 5):
for h in range(0, 3):
for c in range(0, 3):
l += 1
data[b, h, c] = (l % 5 + 1) / 10
output = model.predict(
data, batch_size=5
) # the batch_size has no impact on the result here # the batch_size has no impact on the result here
wrt = js.JSONwriter(model, "tests/test_gru_model.json")
wrt.save()
print(output.shape)
inp = np.zeros((5, 1, 3, 3))
ou = np.zeros((5, 1, 1, 2))
inp[:, 0, :, :] = data[:, :, :]
ou[:, 0, 0, :] = output[:, :]
write("tests/test_gru_input.json", inp)
write("tests/test_gru_output.json", ou)
def gen_simplernn():
model = Sequential()
model.add(
SimpleRNN(4,
activation='linear',
stateful=False,
batch_input_shape=(4, 3, 3)))
model.compile(optimizer='sgd', loss='mse')
data = np.ndarray((4, 3, 3))
kernel = np.ones((3, 4))
rec_kernel = np.ones((4, 4))
bias = np.array([1.0, -1.0, 2.0, -4.0])
k = 0
for h in range(0, 3):
for w in range(0, 4):
k += 1
kernel[h, w] = k % 5 - 2
k = 0
for h in range(0, 4):
for w in range(0, 4):
k += 1
rec_kernel[h, w] = k % 5 - 2
parameters = [kernel, rec_kernel, bias]
model.set_weights(parameters)
l = 0
for b in range(0, 4):
for h in range(0, 3):
for c in range(0, 3):
l += 1
data[b, h, c] = l % 5 + 1
output = model.predict(
data, batch_size=4) # the batch_size has no impact on the result here
wrt = js.JSONwriter(model, "tests/test_simplernn_model.json")
wrt.save()
print(output.shape)
inp = np.zeros((4, 1, 3, 3))
inp[:, 0, :, :] = data[:, :, :]
ou = np.zeros((4, 1, 1, 4))
ou[:, 0, 0, :] = output[:, :]
write("tests/test_simplernn_input.json", inp)
write("tests/test_simplernn_output.json", ou)
def gen_cropping2D_tests():
model = Sequential()
model.add(Cropping2D(cropping=((1, 1), (1, 2)), input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 4, 5, 2), None)
wrt = js.JSONwriter(model, "tests/test_crop_2D_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_crop_2D_input.json", inp)
write("tests/test_crop_2D_output.json", output)
def gen_permute_tests():
model = Sequential()
model.add(Permute((3, 1, 2), input_shape=(2, 3, 4)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 2, 3, 4), None)
wrt = js.JSONwriter(model, "tests/test_permute_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_permute_input.json", inp)
write("tests/test_permute_output.json", output)
def gen_globalaveragepooling2D():
model = Sequential()
model.add(GlobalAveragePooling2D(input_shape=(3, 3, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 3, 3, 2), None)
wrt = js.JSONwriter(model, "tests/test_globalavgpool_2D_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_globalavgpool_2D_input.json", inp)
write("tests/test_globalavgpool_2D_output.json", output)
def gen_maxpool_1D_stride_2():
model = Sequential()
model.add(MaxPooling1D(pool_size=3, strides=2, input_shape=(5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 5, 2), None)
wrt = js.JSONwriter(model, "tests/test_maxpool_1D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_maxpool_1D_2_input.json", inp)
write("tests/test_maxpool_1D_2_output.json", output)
def gen_flatten():
model = Sequential()
model.add(Flatten(input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 4, 5, 2), None)
wrt = js.JSONwriter(model, "tests/test_flat_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_flat_input.json", inp)
write("tests/test_flat_output.json", output)
def gen_repeatvector_tests():
model = Sequential()
model.add(RepeatVector(3, input_shape=(4, )))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((2, 1, 1, 4), None) #np.ndarray((2, 4))
wrt = js.JSONwriter(model, "tests/test_repeatvector_model.json")
wrt.save()
output = np.zeros((2, 1, 3, 4))
output[:, 0, :, :] = model.predict(inp[:, 0, 0, :], batch_size=1)
print(output.shape)
write("tests/test_repeatvector_input.json", inp)
write("tests/test_repeatvector_output.json", output)
def gen_avgpool_2D_stride_1_2():
model = Sequential()
model.add(
AveragePooling2D(pool_size=(3, 4),
strides=(1, 1),
input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, _ = data_generator((1, 4, 5, 2), None)
wrt = js.JSONwriter(model, "tests/test_avgpool_2D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_avgpool_2D_2_input.json", inp)
write("tests/test_avgpool_2D_2_output.json", output)
def gen_repeatvector_tests():
model = Sequential()
model.add(RepeatVector(3, input_shape=(4, )))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((2, 4))
for l in range(0, 4):
inp[0, l] = l + 1
inp[1, l] = -(l + 1)
wrt = js.JSONwriter(model, "tests/test_repeatvector_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_repeatvector_output.json", output.tolist())
def gen_cropping1D_tests():
model = Sequential()
model.add(Cropping1D(cropping=(1, 2), input_shape=(5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 5, 2))
for l in range(0, 5):
inp[0, l, 0] = l + 1
inp[0, l, 1] = -(l + 1)
wrt = js.JSONwriter(model, "tests/test_crop_1D_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_crop_1D_output.json", output.tolist())
def gen_globalmaxpooling2D():
model = Sequential()
model.add(GlobalMaxPooling2D(input_shape=(3, 3, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 3, 3, 2))
inp[0, 0, 0, 0] = 1
inp[0, 1, 0, 0] = 2
inp[0, 2, 0, 0] = 0
inp[0, 0, 1, 0] = 3
inp[0, 1, 1, 0] = 4
inp[0, 2, 1, 0] = 0
inp[0, 0, 2, 0] = 2
inp[0, 1, 2, 0] = 2
inp[0, 2, 2, 0] = 0
inp[0, 0, 0, 1] = 0
inp[0, 1, 0, 1] = 3
inp[0, 2, 0, 1] = 1
inp[0, 0, 1, 1] = 1
inp[0, 1, 1, 1] = 1
inp[0, 2, 1, 1] = -1
inp[0, 0, 2, 1] = -3
inp[0, 1, 2, 1] = -1
inp[0, 2, 2, 1] = 0
wrt = js.JSONwriter(model, "tests/test_globalmaxpool_2D_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_globalmaxpool_2D_output.json", output.tolist())
def gen_cropping2D_tests():
model = Sequential()
model.add(Cropping2D(cropping=((1, 1), (1, 2)), input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 4, 5, 2))
l = 0
for h in range(0, 4):
for w in range(0, 5):
l += 1
inp[0, h, w, 0] = l + 1
inp[0, h, w, 1] = -(l + 1)
wrt = js.JSONwriter(model, "tests/test_crop_2D_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_crop_2D_output.json", output.tolist())
def gen_dropout():
model = Sequential()
model.add(
Convolution2D(8, (2, 2),
strides=(2, 2),
input_shape=(4, 4, 1),
activation='relu'))
model.add(Dropout(rate=0.2))
model.add(Flatten())
model.add(Dense(2, activation='linear'))
model.compile(optimizer='sgd', loss='mse')
inp, _ = data_generator((1, 4, 4, 1), None)
wrt = js.JSONwriter(model, "tests/test_dropout_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_dropout_input.json", inp)
write("tests/test_dropout_output.json", output)
def gen_permute_tests():
model = Sequential()
model.add(Permute((3, 1, 2), input_shape=(2, 3, 4)))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 2, 3, 4))
l = 0
for h in range(0, 2):
for w in range(0, 3):
for c in range(0, 4):
l += 1
inp[0, h, w, c] = l + 1
wrt = js.JSONwriter(model, "tests/test_permute_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_permute_output.json", output.tolist())
def gen_conv_2D_stride_1_1():
model = Sequential()
model.add(Conv2D(2, (3, 4), strides=(1, 1), input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
inp, weight = data_generator((1, 4, 5, 2), (3, 4, 2, 2))
bias = np.ndarray(2)
bias[0] = 0.5
bias[1] = 1.5
w = [weight, bias]
model.set_weights(w)
wrt = js.JSONwriter(model, "tests/test_conv_2D_1_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_conv_2D_1_input.json", inp)
write("tests/test_conv_2D_1_output.json", output)
def gen_batchnorm():
model = Sequential()
model.add(BatchNormalization(input_shape=(2, 1, 3)))
model.compile(optimizer='sgd', loss='mse')
params = [0] * 4
params[0] = np.array([3, 3, 3]) # gamma
params[1] = np.array([1, 2, -1]) # beta
params[2] = np.array([2, 2, 2]) # bias
params[3] = np.array([5, 5, 5]) # variance
inp, _ = data_generator((4, 2, 1, 3), None)
model.set_weights(params)
output = model.predict(inp, batch_size=1)
wrt = js.JSONwriter(model, "tests/test_batchnorm_model.json")
wrt.save()
print(output.shape)
write("tests/test_batchnorm_input.json", inp)
write("tests/test_batchnorm_output.json", output)
def gen_conv_2D_stride_1_2():
model = Sequential()
model.add(Conv2D(2, (2, 4), strides=(2, 1), input_shape=(4, 5, 2)))
model.compile(optimizer='rmsprop', loss='mse')
weight = np.ndarray((2, 4, 2, 2))
weight[0, 0, 0, 0] = 0
weight[0, 0, 0, 1] = 1.5
weight[0, 0, 1, 0] = 2
weight[0, 0, 1, 1] = 0.5
weight[0, 1, 0, 0] = -1
weight[0, 1, 0, 1] = -2
weight[0, 1, 1, 0] = 3
weight[0, 1, 1, 1] = 0
weight[0, 2, 0, 0] = 1
weight[0, 2, 0, 1] = 1
weight[0, 2, 1, 0] = -3
weight[0, 2, 1, 1] = 2.5
weight[0, 3, 0, 0] = 1.5
weight[0, 3, 0, 1] = 0.5
weight[0, 3, 1, 0] = -2
weight[0, 3, 1, 1] = 1.5
weight[1, 0, 0, 0] = -0.5
weight[1, 0, 0, 1] = 2.5
weight[1, 0, 1, 0] = 2.5
weight[1, 0, 1, 1] = 0.5
weight[1, 1, 0, 0] = -1.5
weight[1, 1, 0, 1] = -1
weight[1, 1, 1, 0] = 3
weight[1, 1, 1, 1] = 0.5
weight[1, 2, 0, 0] = 1.5
weight[1, 2, 0, 1] = 1
weight[1, 2, 1, 0] = 0
weight[1, 2, 1, 1] = 0
weight[1, 3, 0, 0] = 1.5
weight[1, 3, 0, 1] = 0
weight[1, 3, 1, 0] = -2
weight[1, 3, 1, 1] = 3
bias = np.ndarray(2)
bias[0] = 0.5
bias[1] = 1.5
w = [weight, bias]
model.set_weights(w)
inp = np.ndarray((1, 4, 5, 2))
inp[0, 0, 0, 0] = 0
inp[0, 0, 0, 1] = 1
inp[0, 0, 1, 0] = 2
inp[0, 0, 1, 1] = 1
inp[0, 0, 2, 0] = 0
inp[0, 0, 2, 1] = 0
inp[0, 0, 3, 0] = 2
inp[0, 0, 3, 1] = 1
inp[0, 0, 4, 0] = 2
inp[0, 0, 4, 1] = 1
inp[0, 1, 0, 0] = 0
inp[0, 1, 0, 1] = -1
inp[0, 1, 1, 0] = 1
inp[0, 1, 1, 1] = -2
inp[0, 1, 2, 0] = 3
inp[0, 1, 2, 1] = 1
inp[0, 1, 3, 0] = 2
inp[0, 1, 3, 1] = 0
inp[0, 1, 4, 0] = 2
inp[0, 1, 4, 1] = -3
inp[0, 2, 0, 0] = 1
inp[0, 2, 0, 1] = 2
inp[0, 2, 1, 0] = -2
inp[0, 2, 1, 1] = 0
inp[0, 2, 2, 0] = 3
inp[0, 2, 2, 1] = -3
inp[0, 2, 3, 0] = 2
inp[0, 2, 3, 1] = 1
inp[0, 2, 4, 0] = 2
inp[0, 2, 4, 1] = 0
inp[0, 3, 0, 0] = 1
inp[0, 3, 0, 1] = 2
inp[0, 3, 1, 0] = 0
inp[0, 3, 1, 1] = -2
inp[0, 3, 2, 0] = 3
inp[0, 3, 2, 1] = 1
inp[0, 3, 3, 0] = 2
inp[0, 3, 3, 1] = 3
inp[0, 3, 4, 0] = -3
inp[0, 3, 4, 1] = 1
wrt = js.JSONwriter(model, "tests/test_conv_2D_2_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_conv_2D_2_output.json", output.tolist())
def gen_tanh():
model = Sequential()
model.add(Flatten(input_shape=(8, 1, 1)))
model.add(Dense(4))
model.add(Activation('tanh'))
model.compile(optimizer='rmsprop', loss='mse')
inp = np.ndarray((1, 8, 1, 1))
inp[0, 0, 0, 0] = 1
inp[0, 1, 0, 0] = 2
inp[0, 2, 0, 0] = -1
inp[0, 3, 0, 0] = 0
inp[0, 4, 0, 0] = 3
inp[0, 5, 0, 0] = 1
inp[0, 6, 0, 0] = 1
inp[0, 7, 0, 0] = 2
weight = np.ndarray((8, 4))
weight[0, 0] = 0
weight[0, 1] = 1.5
weight[0, 2] = 2
weight[0, 3] = 0.5
weight[1, 0] = -1
weight[1, 1] = -2
weight[1, 2] = 3
weight[1, 3] = 0
weight[2, 0] = 1
weight[2, 1] = 1
weight[2, 2] = -3
weight[2, 3] = 2.5
weight[3, 0] = 1.5
weight[3, 1] = 0.5
weight[3, 2] = -2
weight[3, 3] = 1.5
weight[4, 0] = -0.5
weight[4, 1] = 2.5
weight[4, 2] = 2.5
weight[4, 3] = 0.5
weight[5, 0] = -1.5
weight[5, 1] = -1
weight[5, 2] = 3
weight[5, 3] = 0.5
weight[6, 0] = 1.5
weight[6, 1] = 1
weight[6, 2] = 0
weight[6, 3] = 0
weight[7, 0] = 1.5
weight[7, 1] = 0
weight[7, 2] = -2
weight[7, 3] = 3
bias = np.ndarray(4)
bias[0] = 0.5
bias[1] = 1.5
bias[2] = 1.0
bias[3] = 3.0
w = [weight, bias]
model.set_weights(w)
wrt = js.JSONwriter(model, "tests/test_tanh_model.json")
wrt.save()
output = model.predict(inp, batch_size=1)
print(output.shape)
write("tests/test_tanh_output.json", output.tolist())
from keras.optimizers import RMSprop, Adam
import KerasModeltoJSON as js
import numpy as np
import time
model = Sequential()
model.add(Conv2D(32, (8, 8), padding='valid', input_shape=(84, 84, 4), strides=(4, 4)))
model.add(Activation('relu'))
model.add(Conv2D(64, (4, 4), padding='valid', strides=(2, 2)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='valid', strides=(1, 1)))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(6))
# rmsprop = RMSprop(lr=alpha, epsilon=0.01, clipvalue=1.0, decay=0.01)
adam = Adam(lr=0.001)
model.compile(optimizer=adam, loss='mse')
inp = np.random.rand(1, 84, 84, 4)
start = time.process_time()
model.predict(inp, batch_size = 1)
end = time.process_time()
print (end-start)
wrt = js.JSONwriter(model, "tests/test_cnn_model.json")
wrt.save()